consumer research via social media

Consumer Research in Social Media: Guidelines and Recommendations

Series: Methods and Protocols in Food Science > Book: Consumer Research Methods in Food Science

Overview | DOI: 10.1007/978-1-0716-3000-6_14

• Firmenich & Cie SAS, Neuilly-sur-Seine, France
• Independent Consultant, Digital Data Intelligence, Geneva, Switzerland

This chapter focuses on understanding the general practices and some applications of social mediamediaresearchresearches in the food and beverage domain. The first part of the chapter is intended to give a general introduction and define what social

This chapter focuses on understanding the general practices and some applications of social mediamediaresearchresearches in the food and beverage domain. The first part of the chapter is intended to give a general introduction and define what social media is versus other types of media (mainstream media, reviews, etc.). It also explores the human relationship with social media as an extended “digital self,” and why the use of social media has exploded. An additional point is covered with the legal and ethical considerations required to perform researchresearches by using social media monitoring tools. The second and third parts of this chapter focus on general applications of social media researchresearches (exploratory studies, trend watch, netnography, and other applications) and then provide a detailed view of how to perform social media studies step-by-step: from the basic Boolean search formula to natural language processingNatural language processing (NLP) and human analysis. Finally, two case studies are shared to compare the results of social media researchresearches versus online quantitative tests. The objective of this comparison is to explain the strengths and limitations of each research and how they can be complementary, as they are usually compared in both academic and industrial applications.

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• Lunardo R, Jaud DA, Corsi AM (2021) The narcissistic wine consumer: how social attractiveness associated with wine prompts narcissists to engage in wine consumption. Food Qual Prefer 88:104107
• Arellano-Covarrubias A, Gómez-Corona C, Varela P, Escalona-Buendía HB (2019) Connecting flavors in social media: a cross cultural study with beer pairing. Food Res Int 115:303–310
• Simeone M, Scarpato D (2020) Sustainable consumption: how does social media affect food choices? J Clean Prod 277:124036
• Khan S, Umer R, Umer S, Naqvi S (2021) Antecedents of trust in using social media for E-government services: an empirical study in Pakistan. Technol Soc 64:101400
• Simmel G, Wolff KH (1964) The sociology of Georg Simmel. Free Press, Glencoe
• Lewis K, Kaufman J, Gonzalez M, Wimmer A, Christakis N (2008) Tastes, ties, and time: a new social network dataset using Facebook.com. Soc Networks 30(4):330–342
• Sibilia P (2017) La intimidad como espectáculo. FCE, Ciudad de Mexico
• Vidal L, Ares G, Jaeger SR (2018) Application of social media for consumer research. In: Ares G, Varela P (eds) Methods in consumer research. New approaches to classic methods, vol 1. Woodhead Publishing, UK
• Pittman M, Reich B (2016) Social media and loneliness: why an Instagram picture may be worth more than a thousand Twitter words. Comput Hum Behav 62:155–167
• McKee R (2013) Ethical issues in using social media for health and health care research. Health Policy 110:298–301
• Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Bus Horiz 53:59–68
• Edosomwan S, Prakasan SK, Kouame D, Watson J, Seymour T (2011) The history of social media and its impact on business. J Appl Manag Entrep 16(3):79–91
• Stillerman J (2015) The sociology of consumption. A global approach. POLITY, Cambridge
• Prada JM (2018) El ver y las imágenes en el tiempo de internet. AKAL/Estudios Visuales, Madrid
• Toubia O, Stephen AT (2013) Intrinsic vs. image-related utility in social media: why do people contribute content to Twitter? Mark Sci 32(3):368–392
• Balbi G, Magaudda P (2018) A history of digital media: an intermedia and global perspective. Routledge, New York
• Ko MC, Huang HH, Chen HH (2017, August) Paid review and paid writer detection. In: Proceedings of the international conference on web intelligence, pp 637–645
• Choi J, Yoon J, Chung J, Coh BY (2020) Social media analytics and business intelligence research: a systematic review. Inf Process Manag 57:102279
• Kaufman J (2008) Tastes, ties, and time: Facebook data release. Retrieved from: . March 2021 https://cyber.harvard.edu/node/94446
• Zimmer M (2008) More on the “anonymity” of the Facebook dataset – It’s Harvard College (Updated). Retrieved from: . March 2021 https://michaelzimmer.org/2008/10/03/more-on-the-anonymity-of-the-facebook-dataset-its-harvard-college/
• Zimmer M (2010) “But the data is already public”: on the ethics of research in Facebook. Ethics Inf Technol 12:313–325
• Liptak A (2018) The US government alleges Facebook enabled housing ad discrimination. The Verge
• Michaelidou N, Micevski M, Cadogan JHW (2021) Users’ ethical perceptions of social media research: conceptualisation and measurement. J Bus Res 124:684–694
• AOIR – Association of Internet Researchers (2019) Internet research: ethical guidelines 3.0. Retrieved from: . March 2021 https://aoir.org/reports/ethics3.pdf
• GDPR, General Data Protection Regulation (2021) General data protection regulation. Retrieved from: . November 2021 https://gdpr-info.eu/
• Hama Kareem JA, Rashid WN, Abdulla DF, Mahmood OK (2016) Social media and consumer awareness toward manufactured food. Cogent Bus Manag 3(1):1266786
• Hawkins LK, Farrow C, Thomas JM (2020) Do perceived norms of social media users’ eating habits and preferences predict our own food consumption and BMI? Appetite 149:104611
• Beaudoin CE, Hong T (2021) Emotions in the time of coronavirus: antecedents of digital and social media use among Millennials. Comput Hum Behav 123:106876
• Jackson M, Brennan L, Parker L (2021) The public health community’s use of social media for policy advocacy: a scoping review and suggestions to advance the field. Public Health 198:146–155
• Ferdenzi C, Delplanque S, Barbosa P, Court K, Guinard JX, Guo T et al (2013) Affective semantic space of scents. Towards a universal scale to measure self-reported odor-related feelings. Food Qual Prefer 30(2):128–138
• Vrontis D, Makrides A, Christofi M, Thrassou A (2020) Social media influencer marketing: a systematic review, integrative framework and future research agenda. Int J Consum Stud 00:1–28
• Tuikka AM, Chau N, Kimppa KK (2017) Ethical questions related to using netnography as research method. ORBIT J 1(2):1–11
• Kozinets R (1998) On netnography: initial reflections on consumer research investigations of cyberculture. Adv Consum Res 26:366–371
• Valentin D, Gomez-Corona C (2018) Using ethnography in consumer research. In: Ares G, Varela P (eds) Methods in consumer research. New approaches to classic methods, vol 1. Woodhead Publishing, UK
• Spiro ES (2016) Research opportunities at the intersection of social media and survey data. Curr Opin Psychol 9:67–71
• Thanh TV, Kirova V (2018) Wine tourism experience: a netnography study. J Bus Res 83:30–37
• Mkono M, Markwell K, Wilson E (2013) Applying Quan and Wang’s structural model of the tourist experience: a Zimbabwean netnography of food tourism. Tour Manag Perspect 5:68–74
• Irwansyah I, Triputra P (2016) Indonesia gastronomy brand: Netnography on virtual culinary community. Soc Sci 11(19):4585–4588
• Ang L (2011) Community relationship management and social media. J Database Mark Cust Strategy Manag 18(1):31–38
• Dessart L (2017) Social media engagement: a model of antecedents and relational outcomes. J Mark Manag 33(5–6):375–399
• Frants VI, Shapiro J, Taksa I, Voiskunskii VG (1999) Boolean search: current state and perspectives. J Am Soc Inf Sci 50(1):86–95
• Aliyu MB (2017) Efficiency of Boolean search strings for information retrieval. Am J Eng Res 6(11):216–222
• Reinert M (1990) Alceste une méthodologie d’analyse des données textuelles et une application: Aurelia De Gerard De Nerval. Bulletin de Méthodologie Sociologique 26(1):24–54
• Vidal L, Ares G, Machín L, Jaeger SR (2015) Using Twitter data for food-related consumer research: a case study on “what people say when tweeting about different eating situations”. Food Qual Prefer 45:58–69
• Laguna L, Fiszman S, Puerta P, Chaya C, Tárrega A (2020) The impact of COVID-19 lockdown on food priorities. Results from a preliminary study using social media and an online survey with Spanish consumers. Food Qual Prefer 86:104028
• Kuttschreuter M, Rutsaert P, Hilverda F, Regan Á, Barnett J, Verbeke W (2014) Seeking information about food-related risks: the contribution of social media. Food Qual Prefer 37:10–18
• Firmenich SA (2021) COVID-19 consumer behavior survey: wave 3
• Schmoyer JR, Brain J, Koza E (2021) Never average a crisis Better understanding of COVID 19’s impact on food drink behaviors through emotional clustering. In: Poster presented at 14th pangborn sensory science symposium, online, 9–12 August 2021
• Adams MJ, Umbach PD (2012) Nonresponse and online student evaluations of teaching: understanding the influence of salience, fatigue, and academic environments. Res High Educ 53(5):576–591
• DeMaio TJ (1984) Social desirability and survey. Surv Subj Phenom 2:257
• Huang HM (2006) Do print and Web surveys provide the same results? Comput Hum Behav 22(3):334–350

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Customer engagement in social media: a framework and meta-analysis

• Review Paper
• Published: 27 May 2020
• Volume 48 , pages 1211–1228, ( 2020 )

Cite this article

• Fernando de Oliveira Santini 1 ,
• Wagner Junior Ladeira 1 ,
• Diego Costa Pinto   ORCID: orcid.org/0000-0003-4418-9450 2 ,
• Márcia Maurer Herter 3 ,
• Claudio Hoffmann Sampaio 4 &
• Barry J. Babin 5  

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This research examines customer engagement in social media (CESM) using a meta-analytic model of 814 effect sizes across 97 studies involving 161,059 respondents. Findings reveal that customer engagement is driven by satisfaction, positive emotions, and trust, but not by commitment. Satisfaction is a stronger predictor of customer engagement in high (vs. low) convenience, B2B (vs. B2C), and Twitter (vs. Facebook and Blogs). Twitter appears twice as likely as other social media platforms to improve customer engagement via satisfaction and positive emotions. Customer engagement is also found to have substantial value for companies, directly impacting firm performance, behavioral intention, and word-of-mouth. Moreover, hedonic consumption yields nearly three times stronger customer engagement to firm performance effects vis-à-vis utilitarian consumption. However, contrary to conventional managerial wisdom, word-of-mouth does not improve firm performance nor does it mediate customer engagement effects on firm performance. Contributions to customer engagement theory, including an embellishment of the customer engagement mechanics definition, and practical implications for managers are discussed.

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We did examine an alternative model allowing direct effects of trust and commitment on firm performance, behavioral intention and WOM. The chi-square difference between the CESM model and the alternative is 19.9 with 4 df ( p  = .00052). The CFI suggests a slight improvement in fit to 0.98 versus 0.97. The improvement in fit is due largely due to a positive, significant, and nontrivial trust-performance relationship. More importantly, the addition of the direct paths does not affect the parameter estimates to any large degree as the correlation between the CESM estimates and the alternative model is r  = 0.922. The parameter stability further provides evidence of a lack of bias due to interpretational confounding.

Aggarwal, R., Gopal, R., Gupta, A., & Singh, H. (2012). Putting money where the mouths are: The relation between venture financing and electronic word-of-mouth. Information Systems Research , 23 (3-part-2), 976-992.

Agrawal, N., Menon, G., & Aaker, J. L. (2007). Getting emotional about health. Journal of Marketing Research, 44 (1), 100–113.

Article   Google Scholar  

Algesheimer, R., Dholakia, U. M., & Herrmann, A. (2005). The social influence of brand community: Evidence from European car clubs. Journal of Marketing, 69 (3), 19–34.

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.

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.

Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20 (4), 644–656.

Badrinarayanan, V. A., Sierra, J. J., & Martin, K. M. (2015). A dual identification framework of online multiplayer video games: The case of massively multiplayer online role playing games (MMORPGs). Journal of Business Research, 68 (5), 1045–1052.

Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27 , 184–206.

Baldus, B. J., Voorhees, C., & Calantone, R. (2015). Online brand community engagement: Scale development and validation. Journal of Business Research, 68 (5), 978–985.

Beckers, S. F. M., van Doorn, J., & Verhoef, P. C. (2018). Good, better, engaged? The effect of company-initiated customer engagement behavior on shareholder value. Journal of the Academy of Marketing Science, 46 (3), 366–383.

Bijmolt, T. H. A., Leeflang, P. S. H., Block, F., Eisenbeiss, M., Hardie, B. G. S., Lemmens, A., & Saffert, P. (2010). Analytics for customer engagement. Journal of Service Research, 13 (3), 341–356.

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2019). Introduction to meta-analysis . UK: John Wiley & Sons.

Google Scholar  

Bowden, J. L. H. (2009a). The process of customer engagement: A conceptual framework. Journal of Marketing Theory and Practice, 17 (1), 63–74.

Bowden, J. L. H. (2009b). Customer engagement: A framework for assessing customer-brand relationships: The case of the restaurant industry. Journal of Hospitality Marketing & Management, 18 (6), 574–596.

Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14 (3), 252–271.

Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research, 66 (1), 105–114.

Brown, L. G. (1990). Convenience in services marketing. Journal of Services Marketing, 4 (1), 53–59.

Calder, B. J., Malthouse, E. C., & Schaedel, U. (2009). An experimental study of the relationship between online engagement and advertising effectiveness. Journal of Interactive Marketing, 23 (4), 321–331.

Cheung, M. W. (2015). Meta analysis: A structural equation modeling approach . Wiley.

Cheung, M. W., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10 (1), 40–64.

Cheung, C. M., Shen, X. L., Lee, Z. W., & Chan, T. K. (2015). Promoting sales of online games through customer engagement. Electronic Commerce Research and Applications, 14 (4), 241–250.

Chu, S. C., & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30 (1), 47–75.

Claffey, E., & Brady, M. (2017). Examining consumers’ motivations to engage in firm-hosted virtual communities. Psychology & Marketing, 34 (4), 356–375.

Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in online brand communities: A social media perspective. Journal of Product & Brand Management, 24 (1), 28–42.

Dutot, V., & Mosconi, E. (2016). Understanding factors of disengagement within a virtual community: An exploratory study. Journal of Decision Systems, 25 (3), 227–243.

Eisend, M. (2017). The third-person effect in advertising: A meta-analysis. Journal of Advertising, 46 (3), 377–394.

Fischer, E., & Reuber, A. R. (2011). Social interaction via new social media:(how) can interactions on twitter affect effectual thinking and behavior? Journal of Business Venturing, 26 (1), 1–18.

Forbes. (2015). How To Sell Intangibles . Retrieved July 1 st , 2019 from https://www.forbes.com/sites/larrymyler/2015/08/15/how-to-sell-intangibles/#572772477a0a

Forbes. (2018a). Want to Improve Your Social Media Strategy? Avoid These 14 Faux Pas . Retrieved July 1 st , 2019 from https://www.forbes.com/sites/forbesagencycouncil/2018/04/27/want-to-improve-your-social-media-strategy-avoid-these-14-faux-pas/#4178216574d6

Forbes. (2018b). Social Media is Increasing Brand Engagement and Sales . Retrieved July 1 st , 2019 from https://www.forbes.com/sites/tjmccue/2018/06/26/social-media-is-increasing-brand-engagement-and-sales/#35a844717cb3

Forbes. (2018c). Facebook Engagement Sharply Drops 50% Over Last 18 Months . Retrieved July 1 st , 2019 https://www.forbes.com/sites/ryanerskine/2018/08/13/study-facebook-engagement-sharply-drops-50-over-last-18-months/#58a6828b94e8

Forbes (2019). Creating a Marketing Budget for 2020. Retrieved April 1st, 2020 from https://www.forbes.com/sites/theyec/2019/12/19/creating-a-marketing-budget-for-2020/

Forbes (2020a). Why 2020 is a Critical Global Tipping Point for Social Media . Retrieved April 1st, 2020 from https://www.forbes.com/sites/johnkoetsier/2020/02/18/why-2020-is-a-critical-global-tipping-point-for-social-media/

Forbes (2020b). Top Ten Results from the February 2020 CMO Survey . Retrieved April 1st, 2020 from https://www.forbes.com/sites/christinemoorman/2020/02/26/top-ten-results-from-the-february-2020-cmo-survey/

Geyskens, I., Steenkamp, J. B. E., & Kumar, N. (1999). A meta-analysis of satisfaction in marketing channel relationships. Journal of Marketing Research, 36 (2), 223–238.

Geyskens, I., Krishnan, R., Steenkamp, J. B. E., & Cunha, P. V. (2009). A review and evaluation of meta-analysis practices in management research. Journal of Management, 35 (2), 393–419.

Grewal, D., Puccenelli, N., & Monroe, K. B. (2018). Meta-analysis: Integrating accumulated knowledge. Journal of the Academy of Marketing Science, 46 , 9–30.

Gummerus, J., Liljander, V., Weman, E., & Pihlström, M. (2012). Customer engagement in a Facebook brand community. Management Research Review, 35 (9), 857–877.

Gustafsson, A., Johnson, M. D., & Roos, I. (2005). The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention. Journal of Marketing, 69 (4), 210–218.

Hagtvedt, H., & Patrick, V. M. (2009). The broad embrace of luxury: Hedonic potential as a driver of brand extendibility. Journal of Consumer Psychology, 19 (4), 608–618.

Hair Jr., J. F., Babin, B. J., & Krey, N. (2017). Covariance-based structural equation modeling in the journal of advertising: Review and recommendations. Journal of Advertising, 46 (1), 163–177.

Halaszovich, T., & Nel, J. (2017). Customer–brand engagement and Facebook fan-page “like”-intention. Journal of Product & Brand Management, 26 (2), 120–134.

Han, D., Duhachek, A., & Agrawal, N. (2014). Emotions shape decisions through construal level: The case of guilt and shame. Journal of Consumer Research, 41 (4), 1047–1064.

Harmeling, C. M., Moffett, J. W., Arnold, M. J., & Carlson, B. D. (2017). Toward a theory of customer engagement marketing. Journal of the Academy of Marketing Science, 45 (3), 312–335.

Harvard Business Review. (2018). The Basic Social Media Mistakes Companies Still Make . Retrieved July 1 st , 2018 from https://hbr.org/2018/01/the-basic-social-media-mistakes-companies-still-make

Harvard Business Review. (2019). Using Social Media to Connect With Your Most Loyal Customers . Retrieved January 15 th , 2020 from https://hbr.org/2019/12/using-social-media-to-connect-with-your-most-loyal-customers

Harvard Business Review. (2020). Four Questions to Boost Your Social Media Marketing . Retrieved January 15 th , 2020 from https://hbr.org/2020/01/4-questions-to-boost-your-social-media-marketing

Hauk, N., Hüffmeier, J., & Krumm, S. (2018). Ready to be a silver surfer? A meta-analysis on the relationship between chronological age and technology acceptance. Computers in Human Behavior, 84 , 304–319.

Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis . Orlando, FL: Academic Press.

Hennig-Thurau, T., Qwinner, P. K., 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 , 38–52.

Higgins, E. T., & Scholer, A. A. (2009). Engaging the consumer: The science and art of the value creation process. Journal of Consumer Psychology, 19 (2), 100–114.

Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46 , 92–101.

Hollebeek, L. (2011). Exploring customer brand engagement: Definition and themes. Journal of Strategic Marketing, 19 (7), 555–573.

Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing, 28 (2), 149–165.

Hollebeek, L. D., Srivastava, R. K., & Chen, T. (2016). SD logic–informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM. Journal of the Academy of Marketing Science, 47 , 1–25.

Hopp, T., & Gallicano, T. D. (2016). Development and test of a multidimensional scale of blog engagement. Journal of Public Relations Research, 28 (3–4), 127–145.

Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings . SAGE Publications.

Jak, S. (2015). Meta-analytic structural equation modelling . Dordrecht, Neth: Springer.

Book   Google Scholar  

Junco, R., Elavsky, C. M., & Heiberger, G. (2013). Putting twitter to the test: Assessing outcomes for student collaboration, engagement and success. British Journal of Educational Technology, 44 (2), 273–287.

Kim, Y., & Peterson, R. A. (2017). A meta-analysis of online trust relationships in E-commerce. Journal of Interactive Marketing, 38 , 44–54.

Kumar, V. (2013). Profitable customer engagement - concept . Metrics and Strategies: SAGE Publications.

Kumar, V., & Pansari, A. (2015). Measuring the benefits of employee engagement. MIT Sloan Management Review, 56 (4), 67.

Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010). Undervalued or overvalued customers: Capturing total customer engagement value. Journal of Service Research, 13 (3), 297–310.

Kumar, V., Rajan, B., Gupta, S., & Pozza, I. D. (2019). Customer engagement in service. Journal of the Academy of Marketing Science, 47 (1), 138–160.

Labrecque, L. I. (2014). Fostering consumer–brand relationships in social media environments: The role of Parasocial interaction. Journal of Interactive Marketing, 28 , 134–148.

Labroo, A., & Patrick, V. M. (2009). Why happiness helps you see the big picture. Journal of Consumer Research, 35 (5), 800–809.

Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis (Vol. 49). Thousand Oaks, CA: SAGE Publications.

Mano, H., & Oliver, R. L. (1993). Assessing the dimensionality and structure of the consumption experience: Evaluation, feeling, and satisfaction. Journal of Consumer Research, 20 (3), 451–466.

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6 (7), e1000097.

Mollen, A., & Wilson, H. (2010). Engagement, telepresence, and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives. Journal of Business Research, 63 (9), 919–925.

Moorman, C., Deshpande, R., & Zaltman, G. (1993). Factors affecting trust in market research relationships. Journal of Marketing, 57 (1), 81–101.

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58 (3), 20–38.

Morgan, N. A., & Rego, L. L. (2006). The value of different customer satisfaction and loyalty metrics in predicting business performance. Marketing Science, 25 (5), 426–439.

Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., Estabrook, R., Bates, T. C., Maes, H. H., & Boker, S. M. (2016). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81 (2), 535–549.

Netbase Social Analytics. (2020). Social Media Rankings by Industry. Retrieved January 15 th , 2020 from https://www.netbase.com/social-media-rankings/

Obilo, O. O., Chefor, E., & Saleh, A. (2020). Revisiting the consumer brand engagement concept. Journal of Business Research, In Press. https://doi.org/10.1016/j.jbusres.2019.12.023 .

Pansari, A., & Kumar, V. (2017). Customer engagement: The construct, antecedents, and consequences. Journal of the Academy of Marketing Science, 45 (3), 294–311.

Park, C. W., & MacInnis, D. J. (2006). What's in and what's out: Questions on the boundaries of the attitude construct. Journal of Consumer Research, 33 (1), 16–18.

Paruthi, M., & Kaur, H. (2017). Scale development and validation for measuring online engagement. Journal of Internet Commerce, 16 (2), 127–147.

Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90 (1), 175–181.

Rietveld, R., van Dolen, W., Mazloom, M., & Worring, M. (2020). What you feel, is what you like influence of message appeals on customer engagement on Instagram. Journal of Interactive Marketing, 49 , 20–53.

Rosenblad, A. (2009). Introduction to meta-analysis by Michael Borenstein, Larry V. Hedges, Julian PT Higgins. Hannah R. Rothstein. International Statistical Review, 77 (3), 478–479.

Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86 (3), 638–642.

Rosenthal, R., & DiMatteo, M. R. (2001). Meta-analysis: Recent developments in quantitative methods for literature reviews. Annual Review of Psychology, 52 (1), 59–82.

Rust, R. T., & Cooil, B. (1994). Reliability measures for qualitative data: Theory and implications. Journal of Marketing Research, 31 (1), 1–14.

Santini, F., Ladeira, W. J., Sampaio, C. H., & Pinto, D. C. (2018). The brand experience extended model: A meta-analysis. Journal of Brand Management, 25 , 519–535.

See-To, E. W., & Ho, K. K. (2014). Value co-creation and purchase intention in social network sites: The role of electronic word-of-mouth and trust–a theoretical analysis. Computers in Human Behavior, 31 , 182–189.

Simon, F., & Tossan, V. (2018). Does brand-consumer social sharing matter? A relational framework of customer engagement to brand-hosted social media. Journal of Business Research, 85 , 175–184.

Smith, J. B., & Colgate, M. (2007). Customer value creation: A practical framework. Journal of Marketing Theory and Practice, 15 (1), 7–23.

Sprott, D., Czellar, S., & Spangenberg, E. (2009). The importance of a general measure of brand engagement on market behavior: Development and validation of a scale. Journal of Marketing Research, 46 (1), 92–104.

Thomson, M., MacInnis, D. J., & Whan Park, C. (2005). The ties that bind: Measuring the strength of consumers’ emotional attachments to brands. Journal of Consumer Psychology, 15 (1), 77–91.

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.

Tsai, H. T., Huang, H. C., & Chiu, Y. L. (2012). Brand community participation in Taiwan: Examining the roles of individual-, group-, and relationship-level antecedents. Journal of Business Research, 65 (5), 676–684.

Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13 (3), 253–266.

Van Lange, P. A. M., Rusbult, C. E., Drigotas, S. M., & Arriaga, X. B. (1997). Willingness to sacrifice in close relationships. Journal of Personality and Social Psychology, 72 (6), 1373–1395.

Venkatesan, R. (2017). Executing on a customer engagement strategy. Journal of Marketing Theory and Practice, 45 (3), 289–293.

Verhoef, P. C., Franses, P. H., & Hoekstra, J. C. (2002). The effect of relational constructs on customer referrals and number of services purchased from a multiservice provider: Does age of relationship matter? Journal of the Academy of Marketing Science, 30 (3), 202–216.

Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer engagement as a new perspective in customer management. Journal of Service Research, 13 (3), 247–252.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36 , 1–48.

Villanueva, J., Yoo, S., & Hanssens, D. M. (2008). The impact of marketing-induced versus word-of-mouth customer acquisition on customer equity growth. Journal of Marketing Research, 45 (1), 48–59.

Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer relationships beyond purchase. Journal of Marketing Theory and Practice, 20 (2), 122–146.

Wang, Z., & Kim, H. G. (2017). Can social media marketing improve customer relationship capabilities and firm performance? Dynamic capability perspective. Journal of Interactive Marketing, 39 , 15–26.

Wong, H. Y., & Merrilees, B. (2015). An empirical study of the antecedents and consequences of brand engagement. Marketing Intelligence & Planning, 33 (4), 575–591.

Yadav, M. S., & Pavlou, P. A. (2014). Marketing in computer-mediated environments: Research synthesis and new directions. Journal of Marketing, 78 (1), 20–40.

Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12 (3), 341–352.

Zboja, J. J., & Voorhees, C. M. (2006). The impact of brand trust and satisfaction on retailer repurchase intentions. Journal of Services Marketing, 20 (6), 381–390.

Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1985). Problems and strategies in services marketing. Journal of Marketing, 49 (2), 33–46.

Zenith Media (2020). Social media overtakes print to become the third-largest advertising channel . Retrieved April 1st, 2020 from https://www.zenithmedia.com/social-media-overtakes-print-to-become-the-third-largest-advertising-channel/

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de Oliveira Santini, F., Ladeira, W.J., Pinto, D. et al. Customer engagement in social media: a framework and meta-analysis. J. of the Acad. Mark. Sci. 48 , 1211–1228 (2020). https://doi.org/10.1007/s11747-020-00731-5

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6 ways social media impacts consumer behavior

Written by by Jamia Kenan

Published on  November 30, 2023

Reading time  8 minutes

Whether consumers are laughing at their favorite brand’s infotainment content, buying products through live shopping or tuning into a try-on haul, social media is a daily staple in their lives. In The Sprout Social Index™ , we found 54% of consumers say their social media usage has been higher over the last two years than the previous two years.

With more people flocking to networks than ever before, social media and consumer behavior have evolved in lockstep, so understanding how to reach your target audience remains a necessity.

In this article, we’ll discuss the top six ways social media influences consumer behavior and what each means for your brand’s social strategy.

1. Consumers buy directly from social

Index data shows the top reason consumers follow brands on social media is to stay informed about new products/services, followed by getting access to exclusive deals and promotions.

But why is social commerce so popular? One reason is that it meets consumers where they already are. According to data from McKinsey , the majority of consumers use at least three channels for each purchase journey. For many, checking Facebook, Instagram or TikTok daily—whether they’re casually scrolling or searching for new products—has become as routine as brushing their teeth.

Networks continue to experiment with and formalize ecommerce capabilities to bring convenience to consumers and present brands with new revenue streams. For example, TikTok Shop launched in September 2023, enabling users to find and shop for items even more easily.

US annual social commerce sales per buyer are projected to double from $628 million to $1.224 billion in 2027, based on a forecast from Insider Intelligence.

How you can use this insight

Social commerce makes it infinitely easier for brands to deliver the seamless purchase experience buyers want. You can turn a casual scroller into a new customer in a couple of clicks. For example, if you’re a retail business and a holiday is coming up, you can create a shoppable Facebook ad or offer a limited time offer using Instagram Shops for your seasonal product lines.

If you’re not already, look into what social commerce functionality is available on the channels your audience spends the most time on. From TikTok to YouTube livestream shopping , there is a growing number of ways to connect with ready-to-buy consumers.

If you’re a Sprout user, take advantage of our integrations with Shopify and Facebook Shops by connecting your product catalogs with our platform—you can quickly add product links in your outbound posts and customer replies.

2. Consumers expect two-way engagement with brands

Social media adds another dimension to the brand-customer relationship. A brand is no longer a remote, faceless entity that we only learn about in publications, press releases or Google searches. Looking at a brand’s social networks helps you gauge their values, relevant news and offerings, and how they relate to their audience.

Social lets consumers engage and interact with businesses in a multitude of ways, from liking posts and following their accounts to sharing brand-related content, shouting out brand love or asking product questions. And of course, social shopping makes conversions faster.

Don’t be too shy to engage with your audience, jump on relevant trends, ask questions or run polls and Q&As. And don’t forget to respond to direct messages, comments and @-mentions.

The Index found 51% of consumers said the most memorable brands on social respond to customers. Across all age groups, consumers want to know they’re being heard.

Brand authenticity will drive a customer to choose you over a competitor—and stick with you. This means upholding your organization’s claimed values, listening to your audience, discussing what matters to them, anticipating their needs and delivering on the promises you make.

Engagement happens perpetually across multiple channels and formats. With a tool like Sprout’s Smart Inbox, you can set up rules to automatically tag and categorize inbound messages so you never miss an opportunity to engage.

Analyze trends and patterns across these conversations to gain a deeper understanding of your customers. What’s delightful and what’s frustrating them? What are they praising, and what are they criticizing? What are they sharing about your brand and your competitors with their own audiences?

Of course, brands should address complaints and negative inbound messages, but tools like Sprout can help brands get the answers to these questions so they can proactively engage versus reactively. For example, with social listening, you can uncover opportunities to surprise and delight  your customers.

Elicit and listen to feedback and share it with your organization. Channel this feedback to your colleagues across the business from sales and marketing to product and operations to deliver more tailored customer experiences in the future.

3. Consumers turn to social media for customer service

The evolution of social media and consumer behavior has transformed customer service interactions. Before social, consumers could expect to interact with a brand by calling, emailing or visiting locations in person—complete with the infamous wait times to talk to a representative. Today, social is consumers’ preferred choice for sharing feedback and reaching out with a customer support issue or question.

The days of long telephone hold times punctuated by elevator music are dwindling. Consumers with a product question or order issue are much more inclined to reach out via a brand’s Facebook page, X (formerly known as Twitter) @-mention or Instagram direct message. But social media moves fast, which means customers expect faster answers.

Index data shows customer service isn’t just about responding quickly either. Although 76% of consumers value how quickly a brand can respond to their needs, 70% expect a company to provide personalized responses to customer service needs.

Regardless of whether it’s a busy season, customer service teams may already be spread thin or lack resources, which can result in missed messages, slower responses and suboptimal replies. Prevent frustration, reduce delays and improve communication by evolving your approach to social customer service .

Social customer care starts even before a customer reaches out to you. It means getting a clear understanding of what your customer wants from you, reducing room for error and building long-term relationships with your audience.

How can you create and maintain a social customer care strategy? Start by making it easy for customers to find you. Include relevant contact info on your organization’s social media profiles and bios. Make sure you’re monitoring Meta Messenger and direct messages on X, Instagram or TikTok (or consider recruiting a chatbot’s help) if that’s the communication channel your customers flock to most.

If your business has dedicated teams for social media and customer care, collaboration across departments is a must. Implementing a social customer relationship management (CRM ) tool gives you a single source of truth to provide customer service while getting a more holistic view of customer behavior.

Another critical step is proactive message management. If a customer feels like they’re being ignored, they’ll move on to a more attentive competitor. Do you have ways to centralize inbound support messages across different social networks? Can your social customer care agents easily access important client information via CRM or help desk integrations ? Do you have an efficient process for approving replies to customer questions on social?

If you answered “no” to any of these, don’t be afraid to turn to tools like Sprout to help your team work smarter and build stronger customer relationships .

4. Consumers demand authenticity in the age of AI

Index data shows authentic, non-promotional posts are ranked as the number one content type consumers don’t see enough of from brands on social. However, with limited bandwidth and resources, it can be difficult to consistently produce authentic, creative content at scale. Enter: artificial intelligence (AI).

And although 81% of marketers say AI has already had a positive impact on their work, consumers aren’t as eager to jump onto this technology wave. Over a third (42%) of consumers say they are slightly or very apprehensive about the use of AI in social media interactions.

So how does this impact your brand’s content strategy? Consider pulling back on trendjacking and prioritizing original content that’s true to your brand.

Shaping genuine connections and building community can’t be replicated by machines alone, but adding that golden human touch requires time. Leverage AI to handle manual, time-consuming tasks like social media reporting. If you use AI to create spreadsheets and reports, marketers can focus their energy and efforts into developing more impactful content and engagement strategies. Research and identify where to incorporate AI across your teams ’ tasks and workflows.

5. Consumers want more transparency and less performative activism

A few years ago, consumers wanted brands to take a stand on important causes. The latest Index shows only 25% of consumers think brands must speak out on causes and news relevant to their values to be memorable on social.

Consumers want brands to share more about their business values and practices, and how their products are made/sourced—but they aren’t necessarily looking for them to “take a stand” on larger issues. Due to the rise of performative activism, some efforts read as disingenuous and inauthentic. In other words, consumers don’t just want brands to talk about their values, they must walk the walk too.

This slight shift in consumer behavior is an opportunity for social teams to collaborate with colleagues beyond marketing. Work to develop messaging around your company’s supply chain, operations, labor practices and culture that will resonate on social. Consider featuring more employees in your social content such as a behind-the-scenes series, or connect with C-suite executives to refine their social presence and thought leadership on platforms like LinkedIn. And to amplify those efforts even more, implement employee advocacy into your content strategy.

6. Consumers are heavily influenced by social media reviews

Social media is a living document for social proof —which is increasingly a make-or-break factor for buying decisions.

Data from the Yale Center for Customer Insights shows almost 90% of`consumers trust online reviews as much as they trust personal recommendations. And half of consumers 18-54 look for online reviews before deciding to visit a local business.

Even the most dazzling, high-budget television ads can’t always deliver what social media offers for free: authenticity. Consumers take to channels like X and review hubs like Yelp and Google Reviews to praise, champion and criticize different products and businesses. Buyers are more likely to trust this unfiltered peer feedback from people who have already tried a product or engaged with a brand.

From a brand perspective, reviews are key for audience growth and reputation management . Every review post, comment and @-mention is either an opportunity to reflect on ways your business can improve—or a glowing testimonial worth sharing more broadly with your audience.

Online review management is tricky, but it’s a must for maintaining a positive reputation. It’s hard to distill review data from disparate sources into a quantifiable metric. With a social listening tool like Sprout’s, you can easily analyze the sentiment of messages that mention your brand so you can dig into positive, neutral and negative feedback.

Sprout’s review management capabilities ensure you never miss a message (or a chance to engage) by centralizing reviews from Facebook, Glassdoor, Google My Business, TripAdvisor, Yelp, Google Play Store and Apple App Store in one place.

You can also conduct sentiment analysis in Sprout’s Smart Inbox and Reviews feed. Sprout will automatically assign sentiment to messages in your Smart Inbox and Reviews, but you can dig in further by adding filters and custom views.

Social media and consumer behavior: An ongoing transformation

Social media leveled the playing field between buyers and brands. Consumers can learn about and engage with brands more easily, and vice versa. Brands can listen to what matters to their audience at the most individual level and help solve problems faster.

Thanks to social, consumers expect much more from the businesses they support. With the right tools, organizations of any size can rise to the challenge.

Looking to learn more about social media and consumer behavior and the right next steps? Learn more data insights in The Sprout Social Index™ .

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Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective

• Chenyu Gu   ORCID: orcid.org/0000-0001-6059-0573 1 &
• Qiuting Duan 2  

Humanities and Social Sciences Communications volume  11 , Article number:  587 ( 2024 ) Cite this article

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Influencer advertising has emerged as an integral part of social media marketing. Within this realm, consumer engagement is a critical indicator for gauging the impact of influencer advertisements, as it encompasses the proactive involvement of consumers in spreading advertisements and creating value. Therefore, investigating the mechanisms behind consumer engagement holds significant relevance for formulating effective influencer advertising strategies. The current study, grounded in self-determination theory and employing a stimulus-organism-response framework, constructs a general model to assess the impact of influencer factors, advertisement information, and social factors on consumer engagement. Analyzing data from 522 samples using structural equation modeling, the findings reveal: (1) Social media influencers are effective at generating initial online traffic but have limited influence on deeper levels of consumer engagement, cautioning advertisers against overestimating their impact; (2) The essence of higher-level engagement lies in the ad information factor, affirming that in the new media era, content remains ‘king’; (3) Interpersonal factors should also be given importance, as influencing the surrounding social groups of consumers is one of the effective ways to enhance the impact of advertising. Theoretically, current research broadens the scope of both social media and advertising effectiveness studies, forming a bridge between influencer marketing and consumer engagement. Practically, the findings offer macro-level strategic insights for influencer marketing.

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Introduction.

Recent studies have highlighted an escalating aversion among audiences towards traditional online ads, leading to a diminishing effectiveness of traditional online advertising methods (Lou et al., 2019 ). In an effort to overcome these challenges, an increasing number of brands are turning to influencers as their spokespersons for advertising. Utilizing influencers not only capitalizes on their significant influence over their fan base but also allows for the dissemination of advertising messages in a more native and organic manner. Consequently, influencer-endorsed advertising has become a pivotal component and a growing trend in social media advertising (Gräve & Bartsch, 2022 ). Although the topic of influencer-endorsed advertising has garnered increasing attention from scholars, the field is still in its infancy, offering ample opportunities for in-depth research and exploration (Barta et al., 2023 ).

Presently, social media influencers—individuals with substantial follower bases—have emerged as the new vanguard in advertising (Hudders & Lou, 2023 ). Their tweets and videos possess the remarkable potential to sway the purchasing decisions of thousands if not millions. This influence largely hinges on consumer engagement behaviors, implying that the impact of advertising can proliferate throughout a consumer’s entire social network (Abbasi et al., 2023 ). Consequently, exploring ways to enhance consumer engagement is of paramount theoretical and practical significance for advertising effectiveness research (Xiao et al., 2023 ). This necessitates researchers to delve deeper into the exploration of the stimulating factors and psychological mechanisms influencing consumer engagement behaviors (Vander Schee et al., 2020 ), which is the gap this study seeks to address.

The Stimulus-Organism-Response (S-O-R) framework has been extensively applied in the study of consumer engagement behaviors (Tak & Gupta, 2021 ) and has been shown to integrate effectively with self-determination theory (Yang et al., 2019 ). Therefore, employing the S-O-R framework to investigate consumer engagement behaviors in the context of influencer advertising is considered a rational approach. The current study embarks on an in-depth analysis of the transformation process from three distinct dimensions. In the Stimulus (S) phase, we focus on how influencer factors, advertising message factors, and social influence factors act as external stimuli. This phase scrutinizes the external environment’s role in triggering consumer reactions. During the Organism (O) phase, the research explores the intrinsic psychological motivations affecting individual behavior as posited in self-determination theory. This includes the willingness for self-disclosure, the desire for innovation, and trust in advertising messages. The investigation in this phase aims to understand how these internal motivations shape consumer attitudes and perceptions in the context of influencer marketing. Finally, in the Response (R) phase, the study examines how these psychological factors influence consumer engagement behavior. This part of the research seeks to understand the transition from internal psychological states to actual consumer behavior, particularly how these states drive the consumers’ deep integration and interaction with the influencer content.

Despite the inherent limitations of cross-sectional analysis in capturing the full temporal dynamics of consumer engagement, this study seeks to unveil the dynamic interplay between consumers’ psychological needs—autonomy, competence, and relatedness—and their varying engagement levels in social media influencer marketing, grounded in self-determination theory. Through this lens, by analyzing factors related to influencers, content, and social context, we aim to infer potential dynamic shifts in engagement behaviors as psychological needs evolve. This approach allows us to offer a snapshot of the complex, multi-dimensional nature of consumer engagement dynamics, providing valuable insights for both theoretical exploration and practical application in the constantly evolving domain of social media marketing. Moreover, the current study underscores the significance of adapting to the dynamic digital environment and highlights the evolving nature of consumer engagement in the realm of digital marketing.

Literature review

Stimulus-organism-response (s-o-r) model.

The Stimulus-Response (S-R) model, originating from behaviorist psychology and introduced by psychologist Watson ( 1917 ), posits that individual behaviors are directly induced by external environmental stimuli. However, this model overlooks internal personal factors, complicating the explanation of psychological states. Mehrabian and Russell ( 1974 ) expanded this by incorporating the individual’s cognitive component (organism) into the model, creating the Stimulus-Organism-Response (S-O-R) framework. This model has become a crucial theoretical framework in consumer psychology as it interprets internal psychological cognitions as mediators between stimuli and responses. Integrating with psychological theories, the S-O-R model effectively analyzes and explains the significant impact of internal psychological factors on behavior (Koay et al., 2020 ; Zhang et al., 2021 ), and is extensively applied in investigating user behavior on social media platforms (Hewei & Youngsook, 2022 ). This study combines the S-O-R framework with self-determination theory to examine consumer engagement behaviors in the context of social media influencer advertising, a logic also supported by some studies (Yang et al., 2021 ).

Self-determination theory

Self-determination theory, proposed by Richard and Edward (2000), is a theoretical framework exploring human behavioral motivation and personality. The theory emphasizes motivational processes, positing that individual behaviors are developed based on factors satisfying their psychological needs. It suggests that individual behavioral tendencies are influenced by the needs for competence, relatedness, and autonomy. Furthermore, self-determination theory, along with organic integration theory, indicates that individual behavioral tendencies are also affected by internal psychological motivations and external situational factors.

Self-determination theory has been validated by scholars in the study of online user behaviors. For example, Sweet applied the theory to the investigation of community building in online networks, analyzing knowledge-sharing behaviors among online community members (Sweet et al., 2020 ). Further literature review reveals the applicability of self-determination theory to consumer engagement behaviors, particularly in the context of influencer marketing advertisements. Firstly, self-determination theory is widely applied in studying the psychological motivations behind online behaviors, suggesting that the internal and external motivations outlined within the theory might also apply to exploring consumer behaviors in influencer marketing scenarios (Itani et al., 2022 ). Secondly, although research on consumer engagement in the social media influencer advertising context is still in its early stages, some studies have utilized SDT to explore behaviors such as information sharing and electronic word-of-mouth dissemination (Astuti & Hariyawan, 2021 ). These behaviors, which are part of the content contribution and creation dimensions of consumer engagement, may share similarities in the underlying psychological motivational mechanisms. Thus, this study will build upon these foundations to construct the Organism (O) component of the S-O-R model, integrating insights from SDT to further understand consumer engagement in influencer marketing.

Consumer engagement

Although scholars generally agree at a macro level to define consumer engagement as the creation of additional value by consumers or customers beyond purchasing products, the specific categorization of consumer engagement varies in different studies. For instance, Simon and Tossan interpret consumer engagement as a psychological willingness to interact with influencers (Simon & Tossan, 2018 ). However, such a broad definition lacks precision in describing various levels of engagement. Other scholars directly use tangible metrics on social media platforms, such as likes, saves, comments, and shares, to represent consumer engagement (Lee et al., 2018 ). While this quantitative approach is not flawed and can be highly effective in practical applications, it overlooks the content aspect of engagement, contradicting the “content is king” principle of advertising and marketing. We advocate for combining consumer engagement with the content aspect, as content engagement not only generates more traces of consumer online behavior (Oestreicher-Singer & Zalmanson, 2013 ) but, more importantly, content contribution and creation are central to social media advertising and marketing, going beyond mere content consumption (Qiu & Kumar, 2017 ). Meanwhile, we also need to emphasize that engagement is not a fixed state but a fluctuating process influenced by ongoing interactions between consumers and influencers, mediated by the evolving nature of social media platforms and the shifting sands of consumer preferences (Pradhan et al., 2023 ). Consumer engagement in digital environments undergoes continuous change, reflecting a journey rather than a destination (Viswanathan et al., 2017 ).

The current study adopts a widely accepted definition of consumer engagement from existing research, offering operational feasibility and aligning well with the research objectives of this paper. Consumer engagement behaviors in the context of this study encompass three dimensions: content consumption, content contribution, and content creation (Muntinga et al., 2011 ). These dimensions reflect a spectrum of digital engagement behaviors ranging from low to high levels (Schivinski et al., 2016 ). Specifically, content consumption on social media platforms represents a lower level of engagement, where consumers merely click and read the information but do not actively contribute or create user-generated content. Some studies consider this level of engagement as less significant for in-depth exploration because content consumption, compared to other forms, generates fewer visible traces of consumer behavior (Brodie et al., 2013 ). Even in a study by Qiu and Kumar, it was noted that the conversion rate of content consumption is low, contributing minimally to the success of social media marketing (Qiu & Kumar, 2017 ).

On the other hand, content contribution, especially content creation, is central to social media marketing. When consumers comment on influencer content or share information with their network nodes, it is termed content contribution, representing a medium level of online consumer engagement (Piehler et al., 2019 ). Furthermore, when consumers actively upload and post brand-related content on social media, this higher level of behavior is referred to as content creation. Content creation represents the highest level of consumer engagement (Cheung et al., 2021 ). Although medium and high levels of consumer engagement are more valuable for social media advertising and marketing, this exploratory study still retains the content consumption dimension of consumer engagement behaviors.

Theoretical framework

Internal organism factors: self-disclosure willingness, innovativeness, and information trust.

In existing research based on self-determination theory that focuses on online behavior, competence, relatedness, and autonomy are commonly considered as internal factors influencing users’ online behaviors. However, this approach sometimes strays from the context of online consumption. Therefore, in studies related to online consumption, scholars often use self-disclosure willingness as an overt representation of autonomy, innovativeness as a representation of competence, and trust as a representation of relatedness (Mahmood et al., 2019 ).

The use of these overt variables can be logically explained as follows: According to self-determination theory, individuals with a higher level of self-determination are more likely to adopt compensatory mechanisms to facilitate behavior compared to those with lower self-determination (Wehmeyer, 1999 ). Self-disclosure, a voluntary act of sharing personal information with others, is considered a key behavior in the development of interpersonal relationships. In social environments, self-disclosure can effectively alleviate stress and build social connections, while also seeking societal validation of personal ideas (Altman & Taylor, 1973 ). Social networks, as para-social entities, possess the interactive attributes of real societies and are likely to exhibit similar mechanisms. In consumer contexts, personal disclosures can include voluntary sharing of product interests, consumption experiences, and future purchase intentions (Robertshaw & Marr, 2006 ). While material incentives can prompt personal information disclosure, many consumers disclose personal information online voluntarily, which can be traced back to an intrinsic need for autonomy (Stutzman et al., 2011 ). Thus, in this study, we consider the self-disclosure willingness as a representation of high autonomy.

Innovativeness refers to an individual’s internal level of seeking novelty and represents their personality and tendency for novelty (Okazaki, 2009 ). Often used in consumer research, innovative consumers are inclined to try new technologies and possess an intrinsic motivation to use new products. Previous studies have shown that consumers with high innovativeness are more likely to search for information on new products and share their experiences and expertise with others, reflecting a recognition of their own competence (Kaushik & Rahman, 2014 ). Therefore, in consumer contexts, innovativeness is often regarded as the competence dimension within the intrinsic factors of self-determination (Wang et al., 2016 ), with external motivations like information novelty enhancing this intrinsic motivation (Lee et al., 2015 ).

Trust refers to an individual’s willingness to rely on the opinions of others they believe in. From a social psychological perspective, trust indicates the willingness to assume the risk of being harmed by another party (McAllister, 1995 ). Widely applied in social media contexts for relational marketing, information trust has been proven to positively influence the exchange and dissemination of consumer information, representing a close and advanced relationship between consumers and businesses, brands, or advertising endorsers (Steinhoff et al., 2019 ). Consumers who trust brands or social media influencers are more willing to share information without fear of exploitation (Pop et al., 2022 ), making trust a commonly used representation of the relatedness dimension in self-determination within consumer contexts.

Construction of the path from organism to response: self-determination internal factors and consumer engagement behavior

Following the logic outlined above, the current study represents the internal factors of self-determination theory through three variables: self-disclosure willingness, innovativeness, and information trust. Next, the study explores the association between these self-determination internal factors and consumer engagement behavior, thereby constructing the link between Organism (O) and Response (R).

Self-disclosure willingness and consumer engagement behavior

In the realm of social sciences, the concept of self-disclosure willingness has been thoroughly examined from diverse disciplinary perspectives, encompassing communication studies, sociology, and psychology. Viewing from the lens of social interaction dynamics, self-disclosure is acknowledged as a fundamental precondition for the initiation and development of online social relationships and interactive engagements (Luo & Hancock, 2020 ). It constitutes an indispensable component within the spectrum of interactive behaviors and the evolution of interpersonal connections. Voluntary self-disclosure is characterized by individuals divulging information about themselves, which typically remains unknown to others and is inaccessible through alternative sources. This concept aligns with the tenets of uncertainty reduction theory, which argues that during interpersonal engagements, individuals seek information about their counterparts as a means to mitigate uncertainties inherent in social interactions (Lee et al., 2008 ). Self-disclosure allows others to gain more personal information, thereby helping to reduce the uncertainty in interpersonal relationships. Such disclosure is voluntary rather than coerced, and this sharing of information can facilitate the development of relationships between individuals (Towner et al., 2022 ). Furthermore, individuals who actively engage in social media interactions (such as liking, sharing, and commenting on others’ content) often exhibit higher levels of self-disclosure (Chu et al., 2023 ); additional research indicates a positive correlation between self-disclosure and online engagement behaviors (Lee et al., 2023 ). Taking the context of the current study, the autonomous self-disclosure willingness can incline social media users to read advertising content more attentively and share information with others, and even create evaluative content. Therefore, this paper proposes the following research hypothesis:

H1a: The self-disclosure willingness is positively correlated with content consumption in consumer engagement behavior.

H1b: The self-disclosure willingness is positively correlated with content contribution in consumer engagement behavior.

H1c: The self-disclosure willingness is positively correlated with content creation in consumer engagement behavior.

Innovativeness and consumer engagement behavior

Innovativeness represents an individual’s propensity to favor new technologies and the motivation to use new products, associated with the cognitive perception of one’s self-competence. Individuals with a need for self-competence recognition often exhibit higher innovativeness (Kelley & Alden, 2016 ). Existing research indicates that users with higher levels of innovativeness are more inclined to accept new product information and share their experiences and discoveries with others in their social networks (Yusuf & Busalim, 2018 ). Similarly, in the context of this study, individuals, as followers of influencers, signify an endorsement of the influencer. Driven by innovativeness, they may be more eager to actively receive information from influencers. If they find the information valuable, they are likely to share it and even engage in active content re-creation to meet their expectations of self-image. Therefore, this paper proposes the following research hypotheses:

H2a: The innovativeness of social media users is positively correlated with content consumption in consumer engagement behavior.

H2b: The innovativeness of social media users is positively correlated with content contribution in consumer engagement behavior.

H2c: The innovativeness of social media users is positively correlated with content creation in consumer engagement behavior.

Information trust and consumer engagement

Trust refers to an individual’s willingness to rely on the statements and opinions of a target object (Moorman et al., 1993 ). Extensive research indicates that trust positively impacts information dissemination and content sharing in interpersonal communication environments (Majerczak & Strzelecki, 2022 ); when trust is established, individuals are more willing to share their resources and less suspicious of being exploited. Trust has also been shown to influence consumers’ participation in community building and content sharing on social media, demonstrating cross-cultural universality (Anaya-Sánchez et al., 2020 ).

Trust in influencer advertising information is also a key predictor of consumers’ information exchange online. With many social media users now operating under real-name policies, there is an increased inclination to trust information shared on social media over that posted by corporate accounts or anonymously. Additionally, as users’ social networks partially overlap with their real-life interpersonal networks, extensive research shows that more consumers increasingly rely on information posted and shared on social networks when making purchase decisions (Wang et al., 2016 ). This aligns with the effectiveness goals of influencer marketing advertisements and the characteristics of consumer engagement. Trust in the content posted by influencers is considered a manifestation of a strong relationship between fans and influencers, central to relationship marketing (Kim & Kim, 2021 ). Based on trust in the influencer, which then extends to trust in their content, people are more inclined to browse information posted by influencers, share this information with others, and even create their own content without fear of exploitation or negative consequences. Therefore, this paper proposes the following research hypotheses:

H3a: Information trust is positively correlated with content consumption in consumer engagement behavior.

H3b: Information trust is positively correlated with content contribution in consumer engagement behavior.

H3c: Information trust is positively correlated with content creation in consumer engagement behavior.

Construction of the path from stimulus to organism: influencer factors, advertising information factors, social factors, and self-determination internal factors

Having established the logical connection from Organism (O) to Response (R), we further construct the influence path from Stimulus (S) to Organism (O). Revisiting the definition of influencer advertising in social media, companies, and brands leverage influencers on social media platforms to disseminate advertising content, utilizing the influencers’ relationships and influence over consumers for marketing purposes. In addition to consumer’s internal factors, elements such as companies, brands, influencers, and the advertisements themselves also impact consumer behavior. Although factors like the brand image perception of companies may influence consumer behavior, considering that in influencer marketing, companies and brands do not directly interact with consumers, this study prioritizes the dimensions of influencers and advertisements. Furthermore, the impact of social factors on individual cognition and behavior is significant, thus, the current study integrates influencers, advertisements, and social dimensions as the Stimulus (S) component.

Influencer factors: parasocial identification

Self-determination theory posits that relationships are one of the key motivators influencing individual behavior. In the context of social media research, users anticipate establishing a parasocial relationship with influencers, resembling real-life relationships. Hence, we consider the parasocial identification arising from users’ parasocial interactions with influencers as the relational motivator. Parasocial interaction refers to the one-sided personal relationship that individuals develop with media characters (Donald & Richard, 1956 ). During this process, individuals believe that the media character is directly communicating with them, creating a sense of positive intimacy (Giles, 2002 ). Over time, through repeated unilateral interactions with media characters, individuals develop a parasocial relationship, leading to parasocial identification. However, parasocial identification should not be directly equated with the concept of social identification in social identity theory. Social identification occurs when individuals psychologically de-individualize themselves, perceiving the characteristics of their social group as their own, upon identifying themselves as part of that group. In contrast, parasocial identification refers to the one-sided interactional identification with media characters (such as celebrities or influencers) over time (Chen et al., 2021 ). Particularly when individuals’ needs for interpersonal interaction are not met in their daily lives, they turn to parasocial interactions to fulfill these needs (Shan et al., 2020 ). Especially on social media, which is characterized by its high visibility and interactivity, users can easily develop a strong parasocial identification with the influencers they follow (Wei et al., 2022 ).

Parasocial identification and self-disclosure willingness

Theories like uncertainty reduction, personal construct, and social exchange are often applied to explain the emergence of parasocial identification. Social media, with its convenient and interactive modes of information dissemination, enables consumers to easily follow influencers on media platforms. They can perceive the personality of influencers through their online content, viewing them as familiar individuals or even friends. Once parasocial identification develops, this pleasurable experience can significantly influence consumers’ cognitions and thus their behavioral responses. Research has explored the impact of parasocial identification on consumer behavior. For instance, Bond et al. found that on Twitter, the intensity of users’ parasocial identification with influencers positively correlates with their continuous monitoring of these influencers’ activities (Bond, 2016 ). Analogous to real life, where we tend to pay more attention to our friends in our social networks, a similar phenomenon occurs in the relationship between consumers and brands. This type of parasocial identification not only makes consumers willing to follow brand pages but also more inclined to voluntarily provide personal information (Chen et al., 2021 ). Based on this logic, we speculate that a similar relationship may exist between social media influencers and their fans. Fans develop parasocial identification with influencers through social media interactions, making them more willing to disclose their information, opinions, and views in the comment sections of the influencers they follow, engaging in more frequent social interactions (Chung & Cho, 2017 ), even if the content at times may be brand or company-embedded marketing advertisements. In other words, in the presence of influencers with whom they have established parasocial relationships, they are more inclined to disclose personal information, thereby promoting consumer engagement behavior. Therefore, we propose the following research hypotheses:

H4: Parasocial identification is positively correlated with consumer self-disclosure willingness.

H4a: Self-disclosure willingness mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H4b: Self-disclosure willingness mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H4c: Self-disclosure willingness mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Parasocial identification and information trust

Information Trust refers to consumers’ willingness to trust the information contained in advertisements and to place themselves at risk. These risks include purchasing products inconsistent with the advertised information and the negative social consequences of erroneously spreading this information to others, leading to unpleasant consumption experiences (Minton, 2015 ). In advertising marketing, gaining consumers’ trust in advertising information is crucial. In the context of influencer marketing on social media, companies, and brands leverage the social connection between influencers and their fans. According to cognitive empathy theory, consumers project their trust in influencers onto the products endorsed, explaining the phenomenon of ‘loving the house for the crow on its roof.’ Research indicates that parasocial identification with influencers is a necessary condition for trust development. Consumers engage in parasocial interactions with influencers on social media, leading to parasocial identification (Jin et al., 2021 ). Consumers tend to reduce their cognitive load and simplify their decision-making processes, thus naturally adopting a positive attitude and trust towards advertising information disseminated by influencers with whom they have established parasocial identification. This forms the core logic behind the success of influencer marketing advertisements (Breves et al., 2021 ); furthermore, as mentioned earlier, because consumers trust these advertisements, they are also willing to share this information with friends and family and even engage in content re-creation. Therefore, we propose the following research hypotheses:

H5: Parasocial identification is positively correlated with information trust.

H5a: Information trust mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H5b: Information trust mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H5c: Information trust mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Influencer factors: source credibility

Source credibility refers to the degree of trust consumers place in the influencer as a source, based on the influencer’s reliability and expertise. Numerous studies have validated the effectiveness of the endorsement effect in advertising (Schouten et al., 2021 ). The Source Credibility Model, proposed by the renowned American communication scholar Hovland and the “Yale School,” posits that in the process of information dissemination, the credibility of the source can influence the audience’s decision to accept the information. The credibility of the information is determined by two aspects of the source: reliability and expertise. Reliability refers to the audience’s trust in the “communicator’s objective and honest approach to providing information,” while expertise refers to the audience’s trust in the “communicator being perceived as an effective source of information” (Hovland et al., 1953 ). Hovland’s definitions reveal that the interpretation of source credibility is not about the inherent traits of the source itself but rather the audience’s perception of the source (Jang et al., 2021 ). This differs from trust and serves as a precursor to the development of trust. Specifically, reliability and expertise are based on the audience’s perception; thus, this aligns closely with the audience’s perception of influencers (Kim & Kim, 2021 ). This credibility is a cognitive statement about the source of information.

Source credibility and self-disclosure willingness

Some studies have confirmed the positive impact of an influencer’s self-disclosure on their credibility as a source (Leite & Baptista, 2022 ). However, few have explored the impact of an influencer’s credibility, as a source, on consumers’ self-disclosure willingness. Undoubtedly, an impact exists; self-disclosure is considered a method to attempt to increase intimacy with others (Leite et al., 2022 ). According to social exchange theory, people promote relationships through the exchange of information in interpersonal communication to gain benefits (Cropanzano & Mitchell, 2005 ). Credibility, deriving from an influencer’s expertise and reliability, means that a highly credible influencer may provide more valuable information to consumers. Therefore, based on the social exchange theory’s logic of reciprocal benefits, consumers might be more willing to disclose their information to trustworthy influencers, potentially even expanding social interactions through further consumer engagement behaviors. Thus, we propose the following research hypotheses:

H6: Source credibility is positively correlated with self-disclosure willingness.

H6a: Self-disclosure willingness mediates the impact of Source credibility on content consumption in consumer engagement behavior.

H6b: Self-disclosure willingness mediates the impact of Source credibility on content contribution in consumer engagement behavior.

H6c: Self-disclosure willingness mediates the impact of Source credibility on content creation in consumer engagement behavior.

Source credibility and information trust

Based on the Source Credibility Model, the credibility of an endorser as an information source can significantly influence consumers’ acceptance of the information (Shan et al., 2020 ). Existing research has demonstrated the positive impact of source credibility on consumers. Djafarova, in a study based on Instagram, noted through in-depth interviews with 18 users that an influencer’s credibility significantly affects respondents’ trust in the information they post. This credibility is composed of expertise and relevance to consumers, and influencers on social media are considered more trustworthy than traditional celebrities (Djafarova & Rushworth, 2017 ). Subsequently, Bao and colleagues validated in the Chinese consumer context, based on the ELM model and commitment-trust theory, that the credibility of brand pages on Weibo effectively fosters consumer trust in the brand, encouraging participation in marketing activities (Bao & Wang, 2021 ). Moreover, Hsieh et al. found that in e-commerce contexts, the credibility of the source is a significant factor influencing consumers’ trust in advertising information (Hsieh & Li, 2020 ). In summary, existing research has proven that the credibility of the source can promote consumer trust. Influencer credibility is a significant antecedent affecting consumers’ trust in the advertised content they publish. In brand communities, trust can foster consumer engagement behaviors (Habibi et al., 2014 ). Specifically, consumers are more likely to trust the advertising content published by influencers with higher credibility (more expertise and reliability), and as previously mentioned, consumer engagement behavior is more likely to occur. Based on this, the study proposes the following research hypotheses:

H7: Source credibility is positively correlated with information trust.

H7a: Information trust mediates the impact of source credibility on content consumption in consumer engagement behavior.

H7b: Information trust mediates the impact of source credibility on content contribution in consumer engagement behavior.

H7c: Information trust mediates the impact of source credibility on content creation in consumer engagement behavior.

Advertising information factors: informative value

Advertising value refers to “the relative utility value of advertising information to consumers and is a subjective evaluation by consumers.” In his research, Ducoffe pointed out that in the context of online advertising, the informative value of advertising is a significant component of advertising value (Ducoffe, 1995 ). Subsequent studies have proven that consumers’ perception of advertising value can effectively promote their behavioral response to advertisements (Van-Tien Dao et al., 2014 ). Informative value of advertising refers to “the information about products needed by consumers provided by the advertisement and its ability to enhance consumer purchase satisfaction.” From the perspective of information dissemination, valuable advertising information should help consumers make better purchasing decisions and reduce the effort spent searching for product information. The informational aspect of advertising has been proven to effectively influence consumers’ cognition and, in turn, their behavior (Haida & Rahim, 2015 ).

Informative value and innovativeness

As previously discussed, consumers’ innovativeness refers to their psychological trait of favoring new things. Studies have shown that consumers with high innovativeness prefer novel and valuable product information, as it satisfies their need for newness and information about new products, making it an important factor in social media advertising engagement (Shi, 2018 ). This paper also hypothesizes that advertisements with high informative value can activate consumers’ innovativeness, as the novelty of information is one of the measures of informative value (León et al., 2009 ). Acquiring valuable information can make individuals feel good about themselves and fulfill their perception of a “novel image.” According to social exchange theory, consumers can gain social capital in interpersonal interactions (such as social recognition) by sharing information about these new products they perceive as valuable. Therefore, the current study proposes the following research hypothesis:

H8: Informative value is positively correlated with innovativeness.

H8a: Innovativeness mediates the impact of informative value on content consumption in consumer engagement behavior.

H8b: Innovativeness mediates the impact of informative value on content contribution in consumer engagement behavior.

H8c: Innovativeness mediates the impact of informative value on content creation in consumer engagement behavior.

Informative value and information trust

Trust is a multi-layered concept explored across various disciplines, including communication, marketing, sociology, and psychology. For the purposes of this paper, a deep analysis of different levels of trust is not undertaken. Here, trust specifically refers to the trust in influencer advertising information within the context of social media marketing, denoting consumers’ belief in and reliance on the advertising information endorsed by influencers. Racherla et al. investigated the factors influencing consumers’ trust in online reviews, suggesting that information quality and value contribute to increasing trust (Racherla et al., 2012 ). Similarly, Luo and Yuan, in a study based on social media marketing, also confirmed that the value of advertising information posted on brand pages can foster consumer trust in the content (Lou & Yuan, 2019 ). Therefore, by analogy, this paper posits that the informative value of influencer-endorsed advertising can also promote consumer trust in that advertising information. The relationship between trust in advertising information and consumer engagement behavior has been discussed earlier. Thus, the current study proposes the following research hypotheses:

H9: Informative value is positively correlated with information trust.

H9a: Information trust mediates the impact of informative value on content consumption in consumer engagement behavior.

H9b: Information trust mediates the impact of informative value on content contribution in consumer engagement behavior.

H9c: Information trust mediates the impact of informative value on content creation in consumer engagement behavior.

Advertising information factors: ad targeting accuracy

Ad targeting accuracy refers to the degree of match between the substantive information contained in advertising content and consumer needs. Advertisements containing precise information often yield good advertising outcomes. In marketing practice, advertisers frequently use information technology to analyze the characteristics of different consumer groups in the target market and then target their advertisements accordingly to achieve precise dissemination and, consequently, effective advertising results. The utility of ad targeting accuracy has been confirmed by many studies. For instance, in the research by Qiu and Chen, using a modified UTAUT model, it was demonstrated that the accuracy of advertising effectively promotes consumer acceptance of advertisements in WeChat Moments (Qiu & Chen, 2018 ). Although some studies on targeted advertising also indicate that overly precise ads may raise concerns about personal privacy (Zhang et al., 2019 ), overall, the accuracy of advertising information is effective in enhancing advertising outcomes and is a key element in the success of targeted advertising.

Ad targeting accuracy and information trust

In influencer marketing advertisements, due to the special relationship recognition between consumers and influencers, the privacy concerns associated with ad targeting accuracy are alleviated (Vrontis et al., 2021 ). Meanwhile, the informative value brought by targeting accuracy is highlighted. More precise advertising content implies higher informative value and also signifies that the advertising content is more worthy of consumer trust (Della Vigna, Gentzkow, 2010 ). As previously discussed, people are more inclined to read and engage with advertising content they trust and recognize. Therefore, the current study proposes the following research hypotheses:

H10: Ad targeting accuracy is positively correlated with information trust.

H10a: Information trust mediates the impact of ad targeting accuracy on content consumption in consumer engagement behavior.

H10b: Information trust mediates the impact of ad targeting accuracy on content contribution in consumer engagement behavior.

H10c: Information trust mediates the impact of ad targeting accuracy on content creation in consumer engagement behavior.

Social factors: subjective norm

The Theory of Planned Behavior, proposed by Ajzen ( 1991 ), suggests that individuals’ actions are preceded by conscious choices and are underlain by plans. TPB has been widely used by scholars in studying personal online behaviors, these studies collectively validate the applicability of TPB in the context of social media for researching online behaviors (Huang, 2023 ). Additionally, the self-determination theory, which underpins this chapter’s research, also supports the notion that individuals’ behavioral decisions are based on internal cognitions, aligning with TPB’s assertions. Therefore, this paper intends to select subjective norms from TPB as a factor of social influence. Subjective norm refers to an individual’s perception of the expectations of significant others in their social relationships regarding their behavior. Empirical research in the consumption field has demonstrated the significant impact of subjective norms on individual psychological cognition (Yang & Jolly, 2009 ). A meta-analysis by Hagger, Chatzisarantis ( 2009 ) even highlighted the statistically significant association between subjective norms and self-determination factors. Consequently, this study further explores its application in the context of influencer marketing advertisements on social media.

Subjective norm and self-disclosure willingness

In numerous studies on social media privacy, subjective norms significantly influence an individual’s self-disclosure willingness. Wirth et al. ( 2019 ) based on the privacy calculus theory, surveyed 1,466 participants and found that personal self-disclosure on social media is influenced by the behavioral expectations of other significant reference groups around them. Their research confirmed that subjective norms positively influence self-disclosure of information and highlighted that individuals’ cognitions and behaviors cannot ignore social and environmental factors. Heirman et al. ( 2013 ) in an experiment with Instagram users, also noted that subjective norms could promote positive consumer behavioral responses. Specifically, when important family members and friends highly regard social media influencers as trustworthy, we may also be more inclined to disclose our information to influencers and share this information with our surrounding family and friends without fear of disapproval. In our subjective norms, this is considered a positive and valuable interactive behavior, leading us to exhibit engagement behaviors. Based on this logic, we propose the following research hypotheses:

H11: Subjective norms are positively correlated with self-disclosure willingness.

H11a: Self-disclosure willingness mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H11b: Self-disclosure willingness mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H11c: Self-disclosure willingness mediates the impact of subjective norms on content creation in consumer engagement behavior.

Subjective norm and information trust

Numerous studies have indicated that subjective norms significantly influence trust (Roh et al., 2022 ). This can be explained by reference group theory, suggesting people tend to minimize the effort expended in decision-making processes, often looking to the behaviors or attitudes of others as a point of reference; for instance, subjective norms can foster acceptance of technology by enhancing trust (Gupta et al., 2021 ). Analogously, if a consumer’s social network generally holds positive attitudes toward influencer advertising, they are also more likely to trust the endorsed advertisement information, as it conserves the extensive effort required in gathering product information (Chetioui et al., 2020 ). Therefore, this paper proposes the following research hypotheses:

H12: Subjective norms are positively correlated with information trust.

H12a: Information trust mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H12b: Information trust mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H12c: Information trust mediates the impact of subjective norms on content creation in consumer engagement behavior.

Conceptual model

In summary, based on the Stimulus (S)-Organism (O)-Response (R) framework, this study constructs the external stimulus factors (S) from three dimensions: influencer factors (parasocial identification, source credibility), advertising information factors (informative value, Ad targeting accuracy), and social influence factors (subjective norms). This is grounded in social capital theory and the theory of planned behavior. drawing on self-determination theory, the current study constructs the individual psychological factors (O) using self-disclosure willingness, innovativeness, and information trust. Finally, the behavioral response (R) is constructed using consumer engagement, which includes content consumption, content contribution, and content creation, as illustrated in Fig. 1 .

Consumer engagement behavior impact model based on SOR framework.

Materials and methods

Participants and procedures.

The current study conducted a survey through the Wenjuanxing platform to collect data. Participants were recruited through social media platforms such as WeChat, Douyin, Weibo et al., as samples drawn from social media users better align with the research purpose of our research and ensure the validity of the sample. Before the survey commenced, all participants were explicitly informed about the purpose of this study, and it was made clear that volunteers could withdraw from the survey at any time. Initially, 600 questionnaires were collected, with 78 invalid responses excluded. The criteria for valid questionnaires were as follows: (1) Respondents must have answered “Yes” to the question, “Do you follow any influencers (internet celebrities) on social media platforms?” as samples not using social media or not following influencers do not meet the study’s objective, making this question a prerequisite for continuing the survey; (2) Respondents had to correctly answer two hidden screening questions within the questionnaire to ensure that they did not randomly select scores; (3) The total time taken to complete the questionnaire had to exceed one minute, ensuring that respondents had sufficient time to understand and thoughtfully answer each question; (4) Respondents were not allowed to choose the same score for eight consecutive questions. Ultimately, 522 valid questionnaires were obtained, with an effective rate of 87.00%, meeting the basic sample size requirements for research models (Gefen et al., 2011 ). Detailed demographic information of the study participants is presented in Table 1 .

Measurements

To ensure the validity and reliability of the data analysis results in this study, the measurement tools and scales used in this chapter were designed with reference to existing established research. The main variables in the survey questionnaire include parasocial identification, source credibility, informative value, ad targeting accuracy, subjective norms, self-disclosure willingness, innovativeness, information trust, content consumption, content contribution, and content creation. The measurement scale for parasocial identification was adapted from the research of Schramm and Hartmann, comprising 6 items (Schramm & Hartmann, 2008 ). The source credibility scale was combined from the studies of Cheung et al. and Luo & Yuan’s research in the context of social media influencer marketing, including 4 items (Cheung et al., 2009 ; Lou & Yuan, 2019 ). The scale for informative value was modified based on Voss et al.‘s research, consisting of 4 items (Voss et al., 2003 ). The ad targeting accuracy scale was derived from the research by Qiu Aimei et al., 2018 ) including 3 items. The subjective norm scale was adapted from Ajzen’s original scale, comprising 3 items (Ajzen, 2002 ). The self-disclosure willingness scale was developed based on Chu and Kim’s research, including 3 items (Chu & Kim, 2011 ). The innovativeness scale was formulated following the study by Sun et al., comprising 4 items (Sun et al., 2006 ). The information trust scale was created in reference to Chu and Choi’s research, including 3 items (Chu & Choi, 2011 ). The scales for the three components of social media consumer engagement—content consumption, content contribution, and content creation—were sourced from the research by Buzeta et al., encompassing 8 items in total (Buzeta et al., 2020 ).

All scales were appropriately revised for the context of social media influencer marketing. To avoid issues with scoring neutral attitudes, a uniform Likert seven-point scale was used for each measurement item (ranging from 1 to 7, representing a spectrum from ‘strongly disagree’ to ‘strongly agree’). After the overall design of the questionnaire was completed, a pre-test was conducted with 30 social media users to ensure that potential respondents could clearly understand the meaning of each question and that there were no obstacles to answering. This pre-test aimed to prevent any difficulties or misunderstandings in the questionnaire items. The final version of the questionnaire is presented in Table 2 .

Data analysis

Since the model framework of the current study is derived from theoretical deductions of existing research and, while logically constructed, does not originate from an existing research model, this study still falls under the category of exploratory research. According to the analysis suggestions of Hair and other scholars, in cases of exploratory research model frameworks, it is more appropriate to choose Smart PLS for Partial Least Squares Path Analysis (PLS) to conduct data analysis and testing of the research model (Hair et al., 2012 ).

Measurement of model

In this study, careful data collection and management resulted in no missing values in the dataset. This ensured the integrity and reliability of the subsequent data analysis. As shown in Table 3 , after deleting measurement items with factor loadings below 0.5, the final factor loadings of the measurement items in this study range from 0.730 to 0.964. This indicates that all measurement items meet the retention criteria. Additionally, the Cronbach’s α values of the latent variables range from 0.805 to 0.924, and all latent variables have Composite Reliability (CR) values greater than the acceptable value of 0.7, demonstrating that the scales of this study have passed the reliability test requirements (Hair et al., 2019 ). All latent variables in this study have Average Variance Extracted (AVE) values greater than the standard acceptance value of 0.5, indicating that the convergent validity of the variables also meets the standard (Fornell & Larcker, 1981 ). Furthermore, the results show that the Variance Inflation Factor (VIF) values for each factor are below 10, indicating that there are no multicollinearity issues with the scales in this study (Hair, 2009 ).

The current study then further verified the discriminant validity of the variables, with specific results shown in Table 4 . The square roots of the average variance extracted (AVE) values for all variables (bolded on the diagonal) are greater than the Pearson correlation coefficients between the variables, indicating that the discriminant validity of the scales in this study meets the required standards (Fornell & Larcker, 1981 ). Additionally, a single-factor test method was employed to examine common method bias in the data. The first unrotated factor accounted for 29.71% of the variance, which is less than the critical threshold of 40%. Therefore, the study passed the test and did not exhibit serious common method bias (Podsakoff et al., 2003 ).

To ensure the robustness and appropriateness of our structural equation model, we also conducted a thorough evaluation of the model fit. Initially, through PLS Algorithm calculations, the R 2 values of each variable were greater than the standard acceptance value of 0.1, indicating good predictive accuracy of the model. Subsequently, Blindfolding calculations were performed, and the results showed that the Stone-Geisser Q 2 values of each variable were greater than 0, demonstrating that the model of this study effectively predicts the relationships between variables (Dijkstra & Henseler, 2015 ). In addition, through CFA, we also obtained some indicator values, specifically, χ 2 /df = 2.528 < 0.3, RMSEA = 0.059 < 0.06, SRMR = 0.055 < 0.08. Given its sensitivity to sample size, we primarily focused on the CFI, TLI, and NFI values, CFI = 0.953 > 0.9, TLI = 0.942 > 0.9, and NFI = 0.923 > 0.9 indicating a good fit. Additionally, RMSEA values below 0.06 and SRMR values below 0.08 were considered indicative of a good model fit. These indices collectively suggested that our model demonstrates a satisfactory fit with the data, thereby reinforcing the validity of our findings.

Research hypothesis testing

The current study employed a Bootstrapping test with a sample size of 5000 on the collected raw data to explore the coefficients and significance of the paths in the research model. The final test data results of this study’s model are presented in Table 5 .

The current study employs S-O-R model as the framework, grounded in theories such as self-determination theory and theory of planned behavior, to construct an influence model of consumer engagement behavior in the context of social media influencer marketing. It examines how influencer factors, advertisement information factors, and social influence factors affect consumer engagement behavior by impacting consumers’ psychological cognitions. Using structural equation modeling to analyze collected data ( N  = 522), it was found that self-disclosure willingness, innovativeness, and information trust positively influence consumer engagement behavior, with innovativeness having the largest impact on higher levels of engagement. Influencer factors, advertisement information factors, and social factors serve as effective external stimuli, influencing psychological motivators and, consequently, consumer engagement behavior. The specific research results are illustrated in Fig. 2 .

Tested structural model of consumer engagement behavior.

The impact of psychological motivators on different levels of consumer engagement: self-disclosure willingness, innovativeness, and information trust

The research analysis indicates that self-disclosure willingness and information trust are key drivers for content consumption (H1a, H2a validated). This aligns with previous findings that individuals with a higher willingness to disclose themselves show greater levels of engagement behavior (Chu et al., 2023 ); likewise, individuals who trust advertisement information are more inclined to engage with advertisement content (Kim, Kim, 2021 ). Moreover, our study finds that information trust has a stronger impact on content consumption, underscoring the importance of trust in the dissemination of advertisement information. However, no significant association was found between individual innovativeness and content consumption (H3a not validated).

Regarding the dimension of content contribution in consumer engagement, self-disclosure willingness, information trust, and innovativeness all positively impact it (H1b, H2b, and H3b all validated). This is consistent with earlier research findings that individuals with higher self-disclosure willingness are more likely to like, comment on, or share content posted by influencers on social media platforms (Towner et al., 2022 ); the conclusions of this paper also support that innovativeness is an important psychological driver for active participation in social media interactions (Kamboj & Sharma, 2023 ). However, at the level of consumer engagement in content contribution, while information trust also exerts a positive effect, its impact is the weakest, although information trust has the strongest impact on content consumption.

In social media advertising, the ideal outcome is the highest level of consumer engagement, i.e., content creation, meaning consumers actively join in brand content creation, seeing themselves as co-creators with the brand (Nadeem et al., 2021 ). Our findings reveal that self-disclosure willingness, innovativeness, and information trust all positively influence content creation (H1c, H2c, and H3c all validated). The analysis found that similar to the impact on content contribution, innovativeness has the most significant effect on encouraging individual content creation, followed by self-disclosure willingness, with information trust having the least impact.

In summary, while some previous studies have shown that self-disclosure willingness, innovativeness, and information trust are important factors in promoting consumer engagement (Chu et al., 2023 ; Nadeem et al., 2021 ; Geng et al., 2021 ), this study goes further by integrating and comparing all three within the same research framework. It was found that to trigger higher levels of consumer engagement behavior, trust is not the most crucial psychological motivator; rather, the most effective method is to stimulate consumers’ innovativeness, thus complementing previous research. Subsequently, this study further explores the impact of different stimulus factors on various psychological motivators.

The influence of external stimulus factors on psychological motivators: influencer factors, advertisement information factors, and social factors

The current findings indicate that influencer factors, such as parasocial identification and source credibility, effectively enhance consumer engagement by influencing self-disclosure willingness and information trust. This aligns with prior research highlighting the significance of parasocial identification (Shan et al., 2020 ). Studies suggest parasocial identification positively impacts consumer engagement by boosting self-disclosure willingness and information trust (validated H4a, H4b, H4c, and H5a), but not content contribution or creation through information trust (H5b, H5c not validated). Source credibility’s influence on self-disclosure willingness was not significant (H6 not validated), thus negating the mediating effect of self-disclosure willingness (H6a, H6b, H6c not validated). Influencer credibility mainly affects engagement through information trust (H7a, H7b, H7c validated), supporting previous findings (Shan et al., 2020 ).

Advertisement factors (informative value and ad targeting accuracy) promote engagement through innovativeness and information trust. Informative value significantly impacts higher-level content contribution and creation through innovativeness (H8b, H8c validated), while ad targeting accuracy influences consumer engagement at all levels mainly through information trust (H10a, H10b, H10c validated).

Social factors (subjective norms) enhance self-disclosure willingness and information trust, consistent with previous research (Wirth et al., 2019 ; Gupta et al., 2021 ), and further promote consumer engagement across all levels (H11a, H11b, H11c, H12a, H12b, and H12c all validated).

In summary, influencer, advertisement, and social factors impact consumer engagement behavior by influencing psychological motivators, with influencer factors having the greatest effect on content consumption, advertisement content factors significantly raising higher-level consumer engagement through innovativeness, and social factors also influencing engagement through self-disclosure willingness and information trust.

Implication

From a theoretical perspective, current research presents a comprehensive model of consumer engagement within the context of influencer advertising on social media. This model not only expands the research horizon in the fields of social media influencer advertising and consumer engagement but also serves as a bridge between two crucial themes in new media advertising studies. Influencer advertising has become an integral part of social media advertising, and the construction of a macro model aids researchers in understanding consumer psychological processes and behavioral patterns. It also assists advertisers in formulating more effective strategies. Consumer engagement, focusing on the active role of consumers in disseminating information and the long-term impact on advertising effectiveness, aligns more closely with the advertising effectiveness measures in the new media context than traditional advertising metrics. However, the intersection of these two vital themes lacks comprehensive research and a universal model. This study constructs a model that elucidates the effects of various stimuli on consumer psychology and engagement behaviors, exploring the connections and mechanisms through different mediating pathways. By differentiating levels of engagement, the study offers more nuanced conclusions for diverse advertising objectives. Furthermore, this research validates the applicability of self-determination theory in the context of influencer advertising effectiveness. While this psychological theory has been utilized in communication behavior research, its effectiveness in the field of advertising requires further exploration. The current study introduces self-determination theory into the realm of influencer advertising and consumer engagement, thereby expanding its application in the field of advertising communication. It also responds to the call from the advertising and marketing academic community to incorporate more psychological theories to explain the ‘black box’ of consumer psychology. The inclusion of this theory re-emphasizes the people-centric approach of this research and highlights the primary role of individuals in advertising communication studies.

From a practical perspective, this study provides significant insights for adapting marketing strategies to the evolving media landscape and the empowered role of audiences. Firstly, in the face of changes in the communication environment and the empowerment of audience communication capabilities, traditional marketing approaches are becoming inadequate for new media advertising needs. Traditional advertising focuses on direct, point-to-point effects, whereas social media advertising aims for broader, point-to-mass communication, leveraging audience proactivity to facilitate the viral spread of content across online social networks. Secondly, for brands, the general influence model proposed in this study offers guidance for influencer advertising strategy. If the goal is to maximize reach and brand recognition with a substantial advertising budget, partnering with top influencers who have a large following can be an effective strategy. However, if the objective is to maximize cost-effectiveness with a limited budget by leveraging consumer initiative for secondary spread, the focus should be on designing advertising content that stimulates consumer creativity and willingness to innovate. Thirdly, influencers are advised to remain true to their followers. In influencer marketing, influencers attract advertisers through their influence over followers, converting this influence into commercial gain. This influence stems from the trust followers place in the influencer, thus influencers should maintain professional integrity and prioritize the quality of information they share, even when presented with advertising opportunities. Lastly, influencers should assert more control over their relationships with advertisers. In traditional advertising, companies and brands often exert significant control over the content. However, in the social media era, influencers should negotiate more creative freedom in their advertising partnerships, asserting a more equal relationship with advertisers. This approach ensures that content quality remains high, maintaining the trust influencers have built with their followers.

Limitations and future directions

while this study offers valuable insights into the dynamics of influencer marketing and consumer engagement on social media, several limitations should be acknowledged: Firstly, constrained by the research objectives and scope, this study’s proposed general impact model covers three dimensions: influencers, advertisement information, and social factors. However, these dimensions are not limited to the five variables discussed in this paper. Therefore, we call for future research to supplement and explore more crucial factors. Secondly, in the actual communication environment, there may be differences in the impact of communication effectiveness across various social media platforms. Thus, future research could also involve comparative studies and explorations between different social media platforms. Thirdly, the current study primarily examines the direct effects of various factors on consumer engagement. However, the potential interaction effects between these variables (e.g., how influencers’ credibility might interact with advertisement information quality) are not extensively explored. Future research could investigate these complex interrelationships for a more holistic understanding. Lastly, our study, being cross-sectional, offers preliminary insights into the complex and dynamic nature of engagement between social media influencers and consumers, yet it does not incorporate the temporal dimension. The diverse impacts of psychological needs on engagement behaviors hint at an underlying dynamism that merits further investigation. Future research should consider employing longitudinal designs to directly observe how these dynamics evolve over time.

The findings of the current study not only theoretically validate the applicability of self-determination theory in the field of social media influencer marketing advertising research but also broaden the scope of advertising effectiveness research from the perspective of consumer engagement. Moreover, the research framework offers strategic guidance and reference for influencer marketing strategies. The main conclusions of this study can be summarized as follows.

Innovativeness is the key factor in high-level consumer engagement behavior. Content contribution represents a higher level of consumer engagement compared to content consumption, as it not only requires consumers to dedicate attention to viewing advertising content but also to share this information across adjacent nodes within their social networks. This dissemination of information is a pivotal factor in the success of influencer marketing advertisements. Hence, companies and brands prioritize consumers’ content contribution over mere viewing of advertising content (Qiu & Kumar, 2017 ). Compared to content consumption and contribution, content creation is considered the highest level of consumer engagement, where consumers actively create and upload brand-related content, and it represents the most advanced outcome sought by enterprises and brands in advertising campaigns (Cheung et al., 2021 ). The current study posits that to pursue better outcomes in social media influencer advertising marketing, enhancing consumers’ willingness for self-disclosure, innovativeness, and trust in advertising information are effective strategies. However, the crux lies in leveraging the consumer’s subjective initiative, particularly in boosting their innovativeness. If the goal is simply to achieve content consumption rather than higher levels of consumer engagement, the focus should be on fostering trust in advertising information. There is no hierarchy in the efficacy of different strategies; they should align with varying marketing contexts and advertising objectives.

The greatest role of social media influencers lies in attracting online traffic. information trust is the core element driving content consumption, and influencer factors mainly affect consumer engagement behaviors through information trust. Therefore, this study suggests that the primary role of influencers in social media advertising is to attract online traffic, i.e., increase consumer behavior regarding ad content consumption (reducing avoidance of ad content), and help brands achieve the initial goal of making consumers “see and complete ads.” However, their impact on further high-level consumer engagement behaviors is limited. This mechanism serves as a reminder to advertisers not to overestimate the effects of influencers in marketing. Currently, top influencers command a significant portion of the ad budget, which could squeeze the budget for other aspects of advertising, potentially affecting the overall effectiveness of the campaign. Businesses and brands should consider deeper strategic implications when planning their advertising campaigns.

Valuing Advertising Information Factors, Content Remains King. Our study posits that in the social media influencer marketing context, the key to enhancing consumer contribution and creation of advertising content lies primarily in the advertising information factors. In other words, while content consumption is important, advertisers should objectively assess the role influencers play in advertising. In the era of social media, content remains ‘king’ in advertising. This view indirectly echoes the points made in the previous paragraph: influencers effectively perform initial ‘online traffic generation’ tasks in social media, but this role should not be overly romanticized or exaggerated. Whether it’s companies, brands, or influencers, providing consumers with advertisements rich in informational value is crucial to achieving better advertising outcomes and potentially converting consumers into stakeholders.

Subjective norm is an unignorable social influence factor. Social media is characterized by its network structure of information dissemination, where a node’s information is visible to adjacent nodes. For instance, if user A likes a piece of content C from influencer I, A’s follower B, who may not follow influencer I, can still see content C via user A’s page. The aim of marketing in the social media era is to influence a node and then spread the information to adjacent nodes, either secondarily or multiple times (Kumar & Panda, 2020 ). According to the Theory of Planned Behavior, an individual’s actions are influenced by significant others in their lives, such as family and friends. Previous studies have proven the effectiveness of the Theory of Planned Behavior in influencing attitudes toward social media advertising (Ranjbarian et al., 2012 ). Current research further confirms that subjective norms also influence consumer engagement behaviors in influencer marketing on social media. Therefore, in advertising practice, brands should not only focus on individual consumers but also invest efforts in groups that can influence consumer decisions. Changing consumer behavior in the era of social media marketing doesn’t solely rely on the company’s efforts.

As communication technology advances, media platforms will further empower individual communicative capabilities, moving beyond the era of the “magic bullet” theory. The distinction between being a recipient and a transmitter of information is increasingly blurred. In an era where everyone is both an audience and an influencer, research confined to the role of the ‘recipient’ falls short of addressing the dynamics of ‘transmission’. Future research in marketing and advertising should thus focus more on the power of individual transmission. Furthermore, as Marshall McLuhan famously said, “the medium is the extension of man.” The evolution of media technology remains human-centric. Accordingly, future marketing research, while paying heed to media transformations, should emphasize the centrality of the ‘human’ element.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy issues. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

Abbasi AZ, Tsiotsou RH, Hussain K, Rather RA, Ting DH (2023) Investigating the impact of social media images’ value, consumer engagement, and involvement on eWOM of a tourism destination: a transmittal mediation approach. J Retail Consum Serv 71:103231. https://doi.org/10.1016/j.jretconser.2022.103231

Article   Google Scholar  

Ajzen I (2002) Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior 1. J Appl Soc Psychol 32(4):665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x

Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Altman I, Taylor DA (1973) Social penetration: the development of interpersonal relationships. Holt, Rinehart & Winston

Anaya-Sánchez R, Aguilar-Illescas R, Molinillo S, Martínez-López FJ (2020) Trust and loyalty in online brand communities. Span J Mark ESIC 24(2):177–191. https://doi.org/10.1108/SJME-01-2020-0004

Astuti BA, Hariyawan A (2021) Perspectives of social capital and self-determination on e-WOM at millennial generation in Yogyakarta. Integr J Bus Econ 5(1):399475. https://doi.org/10.33019/ijbe.v5i1.338

Bao Z, Wang D (2021) Examining consumer participation on brand microblogs in China: perspectives from elaboration likelihood model, commitment–trust theory and social presence. J Res Interact Mark 15(1):10–29. https://doi.org/10.1108/JRIM-02-2019-0027

Barta S, Belanche D, Fernández A, Flavián M (2023) Influencer marketing on TikTok: the effectiveness of humor and followers’ hedonic experience. J Retail Consum Serv 70:103149. https://doi.org/10.1016/j.jretconser.2022.103149

Bond BJ (2016) Following your “friend”: social media and the strength of adolescents’ parasocial relationships with media personae. Cyberpsych Behav Soc Netw 19(11):656–660. https://doi.org/10.1089/cyber.2016.0355

Breves P, Amrehn J, Heidenreich A, Liebers N, Schramm H (2021) Blind trust? The importance and interplay of parasocial relationships and advertising disclosures in explaining influencers’ persuasive effects on their followers. Int J Advert 40(7):1209–1229. https://doi.org/10.1080/02650487.2021.1881237

Brodie RJ, Ilic A, Juric B, Hollebeek L (2013) Consumer engagement in a virtual brand community: an exploratory analysis. J Bus Res 66(1):105–114. https://doi.org/10.1016/j.jbusres.2011.07.029

Buzeta C, De Pelsmacker P, Dens N (2020) Motivations to use different social media types and their impact on consumers’ online brand-related activities (COBRAs). J Interact Mark 52(1):79–98. https://doi.org/10.1016/j.intmar.2020.04.0

Chen KJ, Lin JS, Shan Y (2021) Influencer marketing in China: The roles of parasocial identification, consumer engagement, and inferences of manipulative intent. J Consum Behav 20(6):1436–1448. https://doi.org/10.1002/cb.1945

Chetioui Y, Benlafqih H, Lebdaoui H (2020) How fashion influencers contribute to consumers’ purchase intention. J Fash Mark Manag 24(3):361–380. https://doi.org/10.1108/JFMM-08-2019-0157

Cheung ML, Pires GD, Rosenberger III PJ, De Oliveira MJ (2021) Driving COBRAs: the power of social media marketing. Mark Intell Plan 39(3):361–376. https://doi.org/10.1108/MIP-11-2019-0583

Cheung MY, Luo C, Sia CL, Chen H (2009) Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. Int J Electron Comm 13(4):9–38. https://doi.org/10.2753/JEC1086-4415130402

Chung S, Cho H (2017) Fostering parasocial relationships with celebrities on social media: Implications for celebrity endorsement. Psychol Mark 34(4):481–495. https://doi.org/10.1002/mar.21001

Chu SC, Choi SM (2011) Electronic word-of-mouth in social networking sites: a cross-cultural study of the United States and China. J Glob Mark 24(3):263–281. https://doi.org/10.1080/08911762.2011.592461

Chu SC, Kim Y (2011) Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. Int J Advert 30(1):47–75. https://doi.org/10.2501/IJA-30-1-047-075

Chu TH, Sun M, Crystal Jiang L (2023) Self-disclosure in social media and psychologicalwell-being: a meta-analysis. J Soc Pers Relat 40(2):576–599. https://doi.org/10.1177/02654075221119429

Cropanzano R, Mitchell MS (2005) Social exchange theory: an interdisciplinary review. J Manag 31(6):874–900. https://doi.org/10.1177/0149206305279602

Della Vigna S, Gentzkow M (2010) Persuasion: empirical evidence. Annu Rev Econ 2(1):643–669. https://doi.org/10.1146/annurev.economics.102308.124309

Dijkstra TK, Henseler J (2015) Consistent and asymptotically normal PLS estimators for linear structural equations. Comput Stat Data 81:10–23. https://doi.org/10.1016/j.csda.2014.07.008

Article   MathSciNet   Google Scholar  

Djafarova E, Rushworth C (2017) Exploring the credibility of online celebrities’ Instagram profiles in influencing the purchase decisions of young female users. Comput Hum Behav 68:1–7. https://doi.org/10.1016/j.chb.2016.11.009

D Horton D, Richard Wohl R (1956) Mass communication and para-social interaction: Observations on intimacy at a distance. Psychiatry 19(3):215–229. https://doi.org/10.1080/00332747.1956.11023049

Ducoffe RH (1995) How consumers assess the value of advertising. J Curr Issues Res Adver 17(1):1–18. https://doi.org/10.1080/10641734.1995.10505022

Fornell C, Larcker DF (1981) Structural equation models with unobservable variables and measurement error: Algebra and statistics. J Mark Res 18(3):382–388. https://doi.org/10.1177/002224378101800313

Gefen D, Straub DW, Rigdon EE (2011) An update and extension to SEM guidelines for administrative and social science research. Mis Quart 35(2):iii–xiv. https://doi.org/10.2307/23044042

Geng S, Yang P, Gao Y, Tan Y, Yang C (2021) The effects of ad social and personal relevance on consumer ad engagement on social media: the moderating role of platform trust. Comput Hum Behav 122:106834. https://doi.org/10.1016/j.chb.2021.106834

Giles DC (2002) Parasocial interaction: a review of the literature and a model for future research. Media Psychol 4(3):279–305. https://doi.org/10.1207/S1532785XMEP0403_04

Gräve JF, Bartsch F (2022) # Instafame: exploring the endorsement effectiveness of influencers compared to celebrities. Int J Advert 41(4):591–622. https://doi.org/10.1080/02650487.2021.1987041

Gupta R, Ranjan S, Gupta A (2021) Consumer’s perceived trust and subjective norms as antecedents of mobile wallets adoption and continuance intention: a technology acceptance approach. Recent Adv Technol Accept Models Theor 211–224. https://doi.org/10.1007/978-3-030-64987-6_13

Habibi MR, Laroche M, Richard MO (2014) The roles of brand community and community engagement in building brand trust on social media. Comput Hum Behav 37:152–161. https://doi.org/10.1016/j.chb.2014.04.016

Hagger MS, Chatzisarantis NL (2009) Integrating the theory of planned behaviour and self‐determination theory in health behaviour: a meta‐analysis. Brit J Health Psych 14(2):275–302. https://doi.org/10.1348/135910708X373959

Haida A, Rahim HL (2015) Social media advertising value: A Study on consumer’s perception. Int Acad Res J Bus Technol 1(1):1–8. https://www.researchgate.net/publication/280325676_Social_Media_Advertising_Value_A_Study_on_Consumer%27s_Perception

Google Scholar  

Hair JF (2009) Multivariate data analysis. Prentice Hall, Upper Saddle River

Hair JF, Ringle CM, Gudergan SP, Fischer A, Nitzl C, Menictas C (2019) Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice. Bus Res 12(1):115–142. https://doi.org/10.1007/s40685-018-0072-4

Hair JF, Sarstedt M, Ringle CM, Mena JA (2012) An assessment of the use of partial least squares structural equation modeling in marketing research. Acad Mark Sci 40:414–433. https://doi.org/10.1007/s11747-011-0261-6

Heirman W, Walrave M, Ponnet K (2013) Predicting adolescents’ disclosure of personal information in exchange for commercial incentives: An application of an extended theory of planned behavior. Cyberpsych Behav Soc Netw16(2):81–87. https://doi.org/10.1089/cyber.2012.0041

Hewei T, Youngsook L (2022) Factors affecting continuous purchase intention of fashion products on social E-commerce: SOR model and the mediating effect. Entertain Comput 41:100474. https://doi.org/10.1016/j.entcom.2021.100474

Hovland CI, Janis IL, Kelley HH (1953) Communication and persuasion. Yale University Press

Hsieh JK, Li YJ (2020) Will you ever trust the review website again? The importance of source credibility. Int J Electron Commerce 24(2):255–275. https://doi.org/10.1080/10864415.2020.1715528

Huang YC (2023) Integrated concepts of the UTAUT and TPB in virtual reality behavioral intention. J Retail Consum Serv 70:103127. https://doi.org/10.1016/j.jretconser.2022.103127

Hudders L, Lou C (2023) The rosy world of influencer marketing? Its bright and dark sides, and future research recommendations. Int J Advert 42(1):151–161. https://doi.org/10.1080/02650487.2022.2137318

Itani OS, Kalra A, Riley J (2022) Complementary effects of CRM and social media on customer co-creation and sales performance in B2B firms: The role of salesperson self-determination needs. Inf Manag 59(3):103621. https://doi.org/10.1016/j.im.2022.103621

Jang W, Kim J, Kim S, Chun JW (2021) The role of engagement in travel influencer marketing: the perspectives of dual process theory and the source credibility model. Curr Issues Tour 24(17):2416–2420. https://doi.org/10.1080/13683500.2020.1845126

Jin SV, Ryu E, Muqaddam A (2021) I trust what she’s# endorsing on Instagram: moderating effects of parasocial interaction and social presence in fashion influencer marketing. J Fash Mark Manag 25(4):665–681. https://doi.org/10.1108/JFMM-04-2020-0059

Kamboj S, Sharma M (2023) Social media adoption behaviour: consumer innovativeness and participation intention. Int J Consum Stud 47(2):523–544. https://doi.org/10.1111/ijcs.12848

Kaushik AK, Rahman Z (2014) Perspectives and dimensions of consumer innovativeness: a literature review and future agenda. J Int Consum Mark 26(3):239–263. https://doi.org/10.1080/08961530.2014.893150

Kelley JB, Alden DL (2016) Online brand community: through the eyes of self-determination theory. Internet Res 26(4):790–808. https://doi.org/10.1108/IntR-01-2015-0017

K Kim DY, Kim HY (2021) Trust me, trust me not: A nuanced view of influencer marketing on social media. J Bus Res 134:223–232. https://doi.org/10.1016/j.jbusres.2021.05.024

Koay KY, Ong DLT, Khoo KL, Yeoh HJ (2020) Perceived social media marketing activities and consumer-based brand equity: Testing a moderated mediation model. Asia Pac J Mark Logist 33(1):53–72. https://doi.org/10.1108/APJML-07-2019-0453

Kumar S, Panda BS (2020) Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach. Phys A: Stat Mech Appl 553:124215. https://doi.org/10.1016/j.physa.2020.124215

Lee D, Hosanagar K, Nair HS (2018) Advertising content and consumer engagement on social media: evidence from Facebook. Manag Sci 64(11):5105–5131. https://doi.org/10.1287/mnsc.2017.2902

Lee DH, Im S, Taylor CR (2008) Voluntary self‐disclosure of information on the Internet: a multimethod study of the motivations and consequences of disclosing information on blogs. Psychol Mark 25(7):692–710. https://doi.org/10.1002/mar.20232

Lee J, Rajtmajer S, Srivatsavaya E, Wilson S (2023) Online self-disclosure, social support, and user engagement during the COVID-19 pandemic. ACM Trans Soc Comput 6(3-4):1–31. https://doi.org/10.1145/3617654

Lee Y, Lee J, Hwang Y (2015) Relating motivation to information and communication technology acceptance: self-determination theory perspective. Comput Hum Behav 51:418–428. https://doi.org/10.1016/j.chb.2015.05.021

Leite FP, Baptista PDP (2022) The effects of social media influencers’ self-disclosure on behavioral intentions: The role of source credibility, parasocial relationships, and brand trust. J Mark Theory Pr 30(3):295–311. https://doi.org/10.1080/10696679.2021.1935275

Leite FP, Pontes N, de Paula Baptista P (2022) Oops, I’ve overshared! When social media influencers’ self-disclosure damage perceptions of source credibility. Comput Hum Behav 133:107274. https://doi.org/10.1016/j.chb.2022.107274

León SP, Abad MJ, Rosas JM (2009) Giving contexts informative value makes information context-specific. Exp Psychol. https://doi.org/10.1027/1618-3169/a000006

Lou C, Tan SS, Chen X (2019) Investigating consumer engagement with influencer-vs. brand-promoted ads: The roles of source and disclosure. J Interact Advert 19(3):169–186. https://doi.org/10.1080/15252019.2019.1667928

Lou C, Yuan S (2019) Influencer marketing: how message value and credibility affect consumer trust of branded content on social media. J Interact Advert 19(1):58–73. https://doi.org/10.1080/15252019.2018.1533501

Luo M, Hancock JT (2020) Self-disclosure and social media: motivations, mechanisms and psychological well-being. Curr Opin Psychol 31:110–115. https://doi.org/10.1016/j.copsyc.2019.08.019

Article   PubMed   Google Scholar  

Mahmood S, Khwaja MG, Jusoh A (2019) Electronic word of mouth on social media websites: role of social capital theory, self-determination theory, and altruism. Int J Space-Based Situat Comput 9(2):74–89. https://doi.org/10.1504/IJSSC.2019.104217

Majerczak P, Strzelecki A (2022) Trust, media credibility, social ties, and the intention to share towards information verification in an age of fake news. Behav Sci 12(2):51. https://doi.org/10.3390/bs12020051

Article   PubMed   PubMed Central   Google Scholar  

McAllister DJ (1995) Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations. Acad Manag J 38(1):24–59. https://doi.org/10.5465/256727

Mehrabian A, Russell JA (1974). An approach to environmental psychology. The MIT Press

Minton EA (2015) In advertising we trust: Religiosity’s influence on marketplace and relational trust. J Advert 44(4):403–414. https://doi.org/10.1080/00913367.2015.1033572

Moorman C, Deshpande R, Zaltman G (1993) Factors affecting trust in market research relationships. J Mark 57(1):81–101. https://doi.org/10.1177/002224299305700106

Muntinga DG, Moorman M, Smit EG (2011) Introducing COBRAs: Exploring motivations for brand-related social media use. Int J Advert 30(1):13–46. https://doi.org/10.2501/IJA-30-1-013-046

Nadeem W, Tan TM, Tajvidi M, Hajli N (2021) How do experiences enhance brand relationship performance and value co-creation in social commerce? The role of consumer engagement and self brand-connection. Technol Forecast Soc 171:120952. https://doi.org/10.1016/j.techfore.2021.120952

Oestreicher-Singer G, Zalmanson L (2013) Content or community? A digital business strategy for content providers in the social age. MIS Quart 37(2):591–616. https://www.jstor.org/stable/43825924

Okazaki S (2009) Social influence model and electronic word of mouth: PC versus mobile internet. Int J Advert 28(3):439–472. https://doi.org/10.2501/S0265048709200692

Piehler R, Schade M, Kleine-Kalmer B, Burmann C (2019) Consumers’ online brand-related activities (COBRAs) on SNS brand pages: an investigation of consuming, contributing and creating behaviours of SNS brand page followers. Eur J Mark 53(9):1833–1853. https://doi.org/10.1108/EJM-10-2017-0722

Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879. https://doi.org/10.1037/0021-9010.88.5.879

Pop RA, Săplăcan Z, Dabija DC, Alt MA (2022) The impact of social media influencers on travel decisions: The role of trust in consumer decision journey. Curr Issues Tour 25(5):823–843. https://doi.org/10.1080/13683500.2021.1895729

Pradhan B, Kishore K, Gokhale N (2023) Social media influencers and consumer engagement: a review and future research agenda. Int J Consum Stud 47(6):2106–2130. https://doi.org/10.1111/ijcs.12901

Qiu A, Chen M (2018) 基于UTAUT修正模型的微信朋友圈广告接受意愿分析 [Analysis of WeChat moments advertising acceptance intention based on a modified UTAUT model]. Stat Decis 34(12):99–102. https://doi.org/10.13546/j.cnki.tjyjc.2018.12.024

Qiu L, Kumar S (2017) Understanding voluntary knowledge provision and content contribution through a social-media-based prediction market: a field experiment. Inf Syst Res 28(3):529–546. https://doi.org/10.1287/isre.2016.0679

Racherla P, Mandviwalla M, Connolly DJ (2012) Factors affecting consumers’ trust in online product reviews. J Consum Behav 11(2):94–104. https://doi.org/10.1002/cb.385

Ranjbarian B, Gharibpoor M, Lari A (2012) Attitude toward SMS advertising and derived behavioral intension, an empirical study using TPB (SEM method). J Am Sci 8(7):297–307. https://www.ceeol.com/search/article-detail?id=466212

Robertshaw GS, Marr NE (2006) The implications of incomplete and spurious personal information disclosures for direct marketing practice. J Database Mark Custom Strategy Manag. 13:186–197. https://doi.org/10.1057/palgrave.dbm.3240296

Roh T, Seok J, Kim Y (2022) Unveiling ways to reach organic purchase: Green perceived value, perceived knowledge, attitude, subjective norm, and trust. J Retail Consum Serv 67:102988. https://doi.org/10.1016/j.jretconser.2022.102988

Schivinski B, Christodoulides G, Dabrowski D (2016) Measuring consumers’ engagement with brand-related social-media content: Development and validation of a scale that identifies levels of social-media engagement with brands. J Advert Res 56(1):64–80. https://doi.org/10.2501/JAR-2016-004

Schouten AP, Janssen L, Verspaget M (2021) Celebrity vs. Influencer endorsements in advertising: the role of identification, credibility, and product-endorser fit. Leveraged marketing communications, Routledge. pp. 208–231

Schramm H, Hartmann T (2008) The PSI-Process Scales. A new measure to assess the intensity and breadth of parasocial processes. Communications. https://doi.org/10.1515/COMM.2008.025

Shan Y, Chen KJ, Lin JS (2020) When social media influencers endorse brands: the effects of self-influencer congruence, parasocial identification, and perceived endorser motive. Int J Advert 39(5):590–610. https://doi.org/10.1080/02650487.2019.1678322

Shi Y (2018) The impact of consumer innovativeness on the intention of clicking on SNS advertising. Mod Econ 9(2):278–285. https://doi.org/10.4236/me.2018.92018

Article   CAS   Google Scholar  

Simon F, Tossan V (2018) Does brand-consumer social sharing matter? A relational framework of customer engagement to brand-hosted social media. J Bus Res 85:175–184. https://doi.org/10.1016/j.jbusres.2017.12.050

Steinhoff L, Arli D, Weaven S, Kozlenkova IV (2019) Online relationship marketing. J Acad Mark Sci 47:369–393. https://doi.org/10.1007/s11747-018-0621-6

Stutzman F, Capra R, Thompson J (2011) Factors mediating disclosure in social network sites. Comput Hum Behav 27(1):590–598. https://doi.org/10.1016/j.chb.2010.10.017

Sun T, Youn S, Wu G, Kuntaraporn M (2006) Online word-of-mouth (or mouse): An exploration of its antecedents and consequences. J Comput-Mediat Comm 11(4):1104–1127. https://doi.org/10.1111/j.1083-6101.2006.00310.x

Sweet KS, LeBlanc JK, Stough LM, Sweany NW (2020) Community building and knowledge sharing by individuals with disabilities using social media. J Comput Assist Lear 36(1):1–11. https://doi.org/10.1111/jcal.12377

Tak P, Gupta M (2021) Examining travel mobile app attributes and its impact on consumer engagement: An application of SOR framework. J Internet Commer 20(3):293–318. https://doi.org/10.1080/15332861.2021.1891517

Towner E, Grint J, Levy T, Blakemore SJ, Tomova L (2022) Revealing the self in a digital world: a systematic review of adolescent online and offline self-disclosure. Curr Opin Psychol 45:101309. https://doi.org/10.1016/j.copsyc.2022.101309

Vander Schee BA, Peltier J, Dahl AJ (2020) Antecedent consumer factors, consequential branding outcomes and measures of online consumer engagement: current research and future directions. J Res Interact Mark 14(2):239–268. https://doi.org/10.1108/JRIM-01-2020-0010

Van-Tien Dao W, Nhat Hanh Le A, Ming-Sung Cheng J, Chao Chen D (2014) Social media advertising value: The case of transitional economies in Southeast Asia. Int J Advert 33(2):271–294. https://doi.org/10.2501/IJA-33-2-271-294

Viswanathan V, Hollebeek LD, Malthouse EC, Maslowska E, Jung Kim S, Xie W (2017) The dynamics of consumer engagement with mobile technologies. Serv Sci 9(1):36–49. https://doi.org/10.1287/serv.2016.0161

Voss KE, Spangenberg ER, Grohmann B (2003) Measuring the hedonic and utilitarian dimensions of consumer attitude. J Mark Res 40(3):310–320. https://doi.org/10.1509/jmkr.40.3.310.19238

Vrontis D, Makrides A, Christofi M, Thrassou A (2021) Social media influencer marketing: A systematic review, integrative framework and future research agenda. Int J Consum Stud 45(4):617–644. https://doi.org/10.1111/ijcs.12647

Wang T, Yeh RKJ, Chen C, Tsydypov Z (2016) What drives electronic word-of-mouth on social networking sites? Perspectives of social capital and self-determination. Telemat Inf 33(4):1034–1047. https://doi.org/10.1016/j.tele.2016.03.005

Watson JB (1917) An Attempted formulation of the scope of behavior psychology. Psychol Rev 24(5):329. https://doi.org/10.1037/h0073044

Wehmeyer ML (1999) A functional model of self-determination: Describing development and implementing instruction. Focus Autism Dev Dis 14(1):53–61. https://www.imdetermined.org/wp-content/uploads/2018/06/SD5_A-Functional-Model-of.pdf

Wei X, Chen H, Ramirez A, Jeon Y, Sun Y (2022) Influencers as endorsers and followers as consumers: exploring the role of parasocial relationship, congruence, and followers’ identifications on consumer–brand engagement. J Interact Advert 22(3):269–288. https://doi.org/10.1080/15252019.2022.2116963

Wirth J, Maier C, Laumer S (2019) Subjective norm and the privacy calculus: explaining self-disclosure on social networking sites. Paper presented at the 27th European Conference on Information Systems (ECIS). Stockholm & Uppsala, Sweden, 8–14, June 2019 https://aisel.aisnet.org/ecis2019_rp

Xiao L, Li X, Zhang Y (2023) Exploring the factors influencing consumer engagement behavior regarding short-form video advertising: a big data perspective. J Retail Consum Serv 70:103170. https://doi.org/10.1016/j.jretconser.2022.103170

Yang J, Peng MYP, Wong S, Chong W (2021) How E-learning environmental stimuli influence determinates of learning engagement in the context of COVID-19? SOR model perspective. Front Psychol 12:584976. https://doi.org/10.3389/fpsyg.2021.584976

Yang K, Jolly LD (2009) The effects of consumer perceived value and subjective norm on mobile data service adoption between American and Korean consumers. J Retail Consum Serv 16(6):502–508. https://doi.org/10.1016/j.jretconser.2009.08.005

Yang S, Zhou S, Cheng X (2019) Why do college students continue to use mobile learning? Learning involvement and self‐determination theory. Brit J Educ Technol 50(2):626–637. https://doi.org/10.1111/bjet.12634

Yusuf AS, Busalim AH (2018) Influence of e-WOM engagement on consumer purchase intention in social commerce. J Serv Mark 32(4):493–504. https://doi.org/10.1108/JSM-01-2017-0031

Zhang G, Yue X, Ye Y, Peng MYP (2021) Understanding the impact of the psychological cognitive process on student learning satisfaction: combination of the social cognitive career theory and SOR model. Front Psychol 12:712323. https://doi.org/10.3389/fpsyg.2021.712323

Zhang J, Liu J, Zhong W (2019) 广告精准度与广告效果:基于隐私关注的现场实验 [Ad targeting accuracy and advertising effectiveness: a field experiment based on privacy concerns]. Manag Sci 32(06):123–132

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Acknowledgements

The authors thank all the participants of this study. The participants were all informed about the purpose and content of the study and voluntarily agreed to participate. The participants were able to stop participating at any time without penalty. Funding for this study was provided by Minjiang University Research Start-up Funds (No. 324-32404314).

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Conceptualization: CG; methodology: CG and QD; software: CG and QD; validation: CG; formal analysis: CG and QD; investigation: CG and QD; resources: CG; data curation: CG and QD; writing—original draft preparation: CG; writing—review and editing: CG; visualization: CG; project administration: CG. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Chenyu Gu .

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Gu, C., Duan, Q. Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective. Humanit Soc Sci Commun 11 , 587 (2024). https://doi.org/10.1057/s41599-024-03127-w

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How (& Where) Consumers Discover Products on Social Media [New Data]

Published: October 03, 2022

Marketing is all about meeting people where they are — and more often than not, they're on social media. For this reason, it's the perfect vehicle for product discovery.

Of course, not all social media platforms are created equal, especially when it comes to product discovery. So, if you're looking to pinpoint the platforms consumers use the most for product research, you've come to the right place.

Here, we'll dive into:

• The top social media channels consumers use for product discovery
• What types of content consumers watch and engage with
• Social media research habits
• Which platforms you should market your products on

Let's dive in.

What is shopper research?

Shopper research examines how, when, and where customers interact with brands to give companies a better understanding of the consumer journey from discovery to conversion.

Shopper research is critical for a better understanding of the customer journey from initial searches to website visits and eventual purchases. Plus, the advent of digital- and mobile-first interactions has made this research even more important as the customer journey now includes multiple paths and touchpoints from start to finish.

For example, prospective buyers might hear about your brand from a friend, do their research on social media, and then interact with your ecommerce store through their mobile device.

Understanding all touchpoints along this journey can help companies create more seamless and streamlined experiences for consumers and increase overall ROI.

The Top Social Media Channels Consumers Use For Product Discovery

22% of consumers prefer to discover new products via social media, according to HubSpot's 2022 State of Consumer Trends Report . Let's take a closer look at the channels they leverage for product discovery :

57% of Gen Z have discovered new products on social media in the past three months, and 71% say it's where they most often discover products.

Almost half (49%) of Gen Z consumers prefer to discover new products via Instagram Stories. This isn't too surprising when you consider 90% of people follow a business on Instagram. On top of that, Gen Z ranks Instagram as their favorite social media app.

In second place, 41% want to discover new products through a short-form video — such as a TikTok or Instagram Reel . Since these platforms pull a younger audience, this adds up.

Millennials

Millennials prefer to discover new products via feed or story posts. This could be anything from an Instagram Story to a Facebook post. Facebook, in particular, is the social app Millennials visit the most, followed closely by YouTube.

On top of that, Millennials also like to discover new products through short-form videos (36%).

Gen X discovers new products on social media more frequently than any other channel, even though it isn’t preferred. Like Millennials, they're most likely to find new products via feed or story posts.

Interestingly, this age group likes to discover new products through short-form videos (41%) in equal measure to Gen Z. It's clear that short, snackable content is appealing to this demographic. In fact, 36% of TikTok users in 2021 were between 35 and 54 years old , a 10% increase from the year before.

Baby Boomers

Social media falls flat for boomers —  a slim 17% have discovered a product on it in the past three months, and only 4% have purchased a product on a social app in that time.

That said, of those who use social media, 42% prefer to discover new products via feed posts. The platforms they visit the most are Facebook, YouTube, Instagram, and Pinterest.

What Types of Product Content Do Consumers Watch or Engage With?

If you're looking to leverage social media, it's a good idea to know what types of branded content consumers enjoy.

Nearly half of consumers (48%) find funny content the most memorable, followed by relatable content. Additionally, m aking content that showcases your product or service — such as a demo, review, or tutorial — is also highly memorable to 36% of consumers.

Social Media Research Habits

To learn more about the social networks people prefer to surf for product research, I conducted a poll of 304 people using Lucid Software.

Source: Lucid Software

At first glance, the survey data seems simple: Facebook is far and away the market leader when it comes to product research and eventual purchasing, followed by YouTube.

But that’s not the whole story. Part of the reason Facebook and YouTube rank so highly is because of their massive user base — for example, Facebook has three times the user base of Instagram, despite being owned by the same company.

It’s also worth noting that while Facebook marketing appeals to a broader audience, volume alone doesn’t guarantee conversion. Users on Pinterest, TikTok, and Reddit tend to be much more engaged with their social community — meaning that if your brand can capture their attention you can create substantive consumer loyalty.

LinkedIn, meanwhile, relies on authenticity and authority to inspire confidence, while Twitter is all about what’s trending right now.

Which social media platform should you market products on?

1. facebook.

Facebook has a whopping 2.7 billion active daily users and has been around since the early 2000s. Its audience includes multiple age groups and spans the globe , making it a solid place for most brands to market themselves.

Most importantly, 78% of US consumers discover new products on Facebook, more than any than other platform.

When it comes to marketing your product, you have many free and paid options on Facebook. Here are a few examples of each.

Free Promotion

By now, you probably know that any company can create a Facebook Business Page . Once you create a business page, you can share posts about your products and offerings. If you have happy customers, you can even ask them to review your business on Facebook so prospects researching you can see how you've pleased your customers in the past.

Aside from creating a page to highlight your brand, you can also post your products in Facebook's Marketplace . Marketplace listings can include product shots, pricing, product specifications, and purchasing information. Although individual users often use the Marketplace to sell items they no longer want to other people, Facebook Business pages are also eligible to use this feature.

You should also consider talking about your products or offerings on Facebook Stories . This might take a little extra effort because it will require you to film or create content in the Story format, but it can help you better connect with prospective buyers who want a better sense of what your brand is about.

Paid Promotion

Because Facebook's feed algorithmically favors posts from individual accounts over businesses, you might decide that you want to put money into Facebook Ads.

Facebook Ads has a solid track record. It's estimated that 10 million businesses were advertising on the platform in 2021.

With Facebook Ads , you can create advertisements with a certain goal in mind, such as conversions or in-store foot traffic. The detailed ads software also allows you to target specific audience demographics.

As a Facebook advertiser , you can either promote a post you've already created to ensure that it shows up on feeds of users in your demographic, or you can create native ads that might show up in feeds or on Facebook's sidebars. While promoted posts look like an average post with a simple tag stating they're promoted, the native ads look more like traditional ads to make it clear to users that the content they're seeing is paid for.

If you want to launch video-based ads, Facebook also allows you to promote video content or buy in-stream ad placements that appear in Facebook Live videos or longer videos that other users have uploaded.

If how-tos or video tutorials are part of your content marketing strategy. YouTube will be a natural fit for your brand. This is because YouTube users are three times more likely to prefer watching a YouTube tutorial video compared to reading the product’s instructions.

YouTube is also popular across multiple age groups. In the last three months, 83% of Millennials have visited YouTube, followed by 81% of Gen Z , and 79% of Gen X . For Baby Boomers, YouTube is their second favorite social media app, just behind Facebook.

With a branded YouTube channel, you can publish video content such as demos, tutorials, or customer testimonial videos that give insightful details about why your product is valuable. By filming your own videos, you can insure that you're highlighting all the great aspects of your product that make it stand out from its competitors.

Alternatively, if you don't have time to create your own videos, sponsoring an influencer's content, tutorial, or review related to your product allows you to tap into that content creator's audience as they tell their followers more about your offerings.

Aside from creating your own account or hiring an influencer to give a review or tutorial, you could also consider paid advertisements. YouTube offers a few ad styles including TrueView, Preroll, and Bumpers .

These ads allow you to submit a short video ad to YouTube which is then placed at the beginning or in the middle of videos with metrics and demographics that match your brand's target. To learn the ins and outs of setting up an ad and determining which style is right for you, check out this guide .

3. Instagram

Although Instagram ranked in third place in the poll above, you shouldn't disregard it — especially if you're targeting Gen-Z or millennials who make up the platform's primary audience .

For years, Instagram's visual layout has made it a hot spot for influencer marketing . Influencers regularly post sponsored photos and videos about their experiences with products. Like YouTube, these influencers also regularly publish video posts or Stories that present tutorials, reviews, and unboxings related to a product.

Aside from influencer marketing, many brands also promote their products on Instagram Stories , Instagram Live , and through standard video or photo posts on Instagram Feed.

Here's an example where Kylie Jenner, the CEO and Founder of Kylie Cosmetics, films a Story-based product tutorial for her company's Instagram account:

Along with free strategies, Instagram now offers Shoppable posts. With Shoppable posts, you can promote a product in an Instagram post that links to your Facebook Catalog . Here's an example of what a Shoppable Post looks like:

To be eligible for Shoppable posts, you must have an Instagram Business page that's linked to a Facebook Catalog. This feature is also only for businesses selling physical goods.

Here's a blog post that goes into detail about how to use and optimize Shoppable posts.

4. Pinterest

Pinterest encourages people to pin image-based posts that inspire them to digital boards, mimicking the process of creating a physical inspiration board.

Because people come to this platform to be inspired to do something, such as travel or home decorating, they might find themselves pinning all sorts of product-oriented images to a themed board. For example, someone who wants to redecorate their office might create an "Office Inspiration" board and pin photos of furniture or decorative items that they'd like to buy.

Here's an example of what these boards look like:

To make it easier for people to find your products, you could consider starting a Pinterest account and making a few boards to highlight your products. For example, if you're marketing a travel company, you could make a board for each country that you offer packages to. On each board, you could place images of trip activities that link to your website.

Then, if someone is trying to plan a trip to a country you sell a package for, they might come across one of your posts and pin it to their own "Travel Inspiration" board.

To give you a real-world example of how brands use Pinterest, below is a Wedding Registry board created by Target which features images of products that a bride and groom might want to add to their gift registry.

Each of Target's pinned images links to the company website so users can share the pin on their own Pinterest board, or click straight through the post to buy or register the product.

If you have an advertising budget, you can also consider launching pay-per-click ads on Pinterest . Pinterest Ads enables your posts to be seen by people in a specific demographic that matches your own. The platform also allows you to A/B test photos and target ads to Pinterest users on your contact lists.

Want to learn more about Pinterest Ads and effective experiments to run? Check out this blog post from a PPC and Pinterest expert.

Reddit encourages users to create discussion threads in themed online communities, called subreddits. As the platform has evolved, many users have created both threads and subreddits devoted to talking about products, like fast-food restaurants or video games.

Below is an example of a subreddit, or online community, that Reddit users created to talk about all things related to Xbox One.

However, because comments with promotional language in them often get downvoted or buried in feeds by more engaging Reddit threads, you'll need to be creative if you want to engage with audiences on this platform.

While you might want to keep an eye on Reddit or experiment with it, don't put all of your time and resources into it — at least right now. As it evolves, the platform may become an easier platform to market your brand on, but at the moment, Reddit marketing strategies still require more brainstorming and time than tactics on other social platforms.

Although this platform has been called one of the "trickiest" for marketers to crack, some bigger brands have figured out how to reach the platform's discussion-oriented users.

For example, some brands will create subreddits related to their product , while others will interact by commenting on threads related to their industry.

Aside from creating content for free on Reddit, you can alternatively pay into sponsored posts or ads, similarly to Facebook or Twitter. These ads will appear in a user's feed or as a promoted comment in a thread or subreddit.

To learn more about the ins and outs of Reddit marketing, click here for tips and examples of how other brands have cultivated the platform.

6. LinkedIn

LinkedIn's platform, which emphasizes networking and career-related chatter, might be well-suited for product marketing in B2B, academic, or professional industries . People who do product research on this platform might be looking for a service, tool, or software that can either escalate their careers or make their workdays easier.

If you're marketing products like software, online courses, business-related publications, or anything that can help a professional or student do their job better, LinkedIn will be a great fit for you. However, if you sell more general, consumer-facing products like makeup or home decorations, you might want to put more marketing effort into other platforms on this list — like Facebook or Instagram.

While the professional nature of LinkedIn and its audience might not be suited for all brands, the platform still offers a variety of opportunities for brands to leverage it. For example, research shows that 80% of B2B leads come straight from LinkedIn .

LinkedIn is very similar to Facebook in that you can post about your product or service for free, or purchase ads or post promotion to get information about your business front and center on feeds. To see a few great ad examples, check out this post .

Image Source

Twitter has approximately 200 million daily users from a variety of backgrounds, geographic locations, and industries. Its broad demographic might provide solid marketing opportunities to many different types of businesses. Because of its broad user base, you might want to create an account on Twitter and post regularly for brand awareness.

If you're interested in video marketing, you can also experiment with Twitter's live video feature and use it to film a tutorial or Q&A related to your product.

Aside from posting about your product for free, you can also pay into targeted ads or promoted tweets . Twitter claims that its advertising ROI is 40% higher than some other social channels .

While the ROI of Twitter advertising and its user base sounds promising, you might be wondering why it ranked so low on the poll shown above.

Ultimately, what might make Twitter rank last is its trend-oriented nature. The platform encourages people to connect with each other and post tweets or comments about current events, trending hashtags, or their thoughts on other specific topics.

Brands and product discussion are both prevalent on the platform, but users might go to Twitter to learn more about what's going on in the world, rather than new products. When people are asked to pick which platform they do the most product research on, it's not surprising that Facebook or YouTube might seem like a more obvious choice than Twitter.

While you should be on Twitter due to its sheer user base and advertising ROI, you'll want to keep its audience's need to stay trendy and informed in mind as you're creating posts and advertisements for the platform. This might help you make social content that both engages these audiences while still weaving in information about how valuable your product is.

Identifying the Right Platforms for Product Marketing

While running ads and product promotions on any social platform can help drive conversion, it’s a good idea to focus on platforms with audiences that already align well with your brand.

For example, broader audiences are actively looking for products or researching brands on Facebook, Instagram, YouTube, and Pinterest while Reddit and Twitter users tend to be more trend-focused. Similarly, if you're marketing a B2B company, you might see a better ROI from ads on a professional network — like LinkedIn — than ads on a more consumer-friendly platform like Instagram.

Use the information provided above, and start leveraging social media for lead conversion and product marketing.

Don't forget to share this post!

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Your Guide to the Marketing Jungle

How to Use Social Media for Customer Research

Social media can provide a free treasure trove of data about your customers.

With the right social media tools, you can learn what questions your customers have and which types of content they're sharing .

This information will help you answer their questions, solve their problems and define your social media and content strategies.

In this post, you'll learn how to quickly conduct research on social media and put it into action.

Find Out What Questions Your Customers Are Asking

Hearing the questions your prospective customers are asking and problems they're facing can help you find new customers, support existing ones and outline a content strategy that will appeal to both .

The best products solve problems. If you can solve someone's problem, they'll be much more likely to check out your products, because they already trust you as an expert.

Here are  three places to easily find which types of questions are being asked .

Twitter Search

Twitter Search is the perfect place to start looking for customer questions. Just search Twitter for your topic or niche and include a “?” in the search.

For example, searches for “Facebook marketing ?” or “yoga ?” will return any tweet with those keywords and a question mark.

From here, you can answer Twitter users' questions with your industry expertise. Remember to be helpful, not self-promotional .

You'll also find people with problems that your product or service can solve . In those cases, feel free to suggest your product. Make sure to provide helpful information, including why your solution is best.

Curious About How to Use AI?

We recently launched a new show that help marketers, creators, and entrepreneurs understand the business applications of AI. It's hosted by Michael Stelzner and explores this exciting new frontier in easy-to-understand terms. Pull up your favorite podcast app and search for AI Explored. (Look for the cover art shown to the right.) Or click the button below for more information.

Quora is the highest-quality question and answer (Q&A) site online. Technology and startups were the first topics to gain traction, but Quora now has great content on everything from cooking to film.

To see how Quora works , start by typing a question into the search bar . You'll see previous questions that match yours as well as a link to add a new question. Just click on an existing question for an example of how the site works.

Next, type your business's main keyword or niche into the search bar and find the most relevant topic , not question.

Click that topic to navigate to the topic page . From here, you'll see all of the questions on a given subject. You can read answers from other users , contribute your own, follow the topic on Quora or set up an RSS feed of questions.

To set up an RSS feed, just add “/rss” to the end of the URL. For example, the topic page for yoga is http://www.quora.com/Yoga . The RSS feed for this page is http://www.quora.com/Yoga/rss . Now you can automatically monitor a Quora topic from your RSS feed reader and contribute where appropriate .

The questions you find here can help you define your business offering and improve your marketing strategies.

LinkedIn Answers

LinkedIn Answers may be the best social media research site for B2B companies. The Q&A section of LinkedIn's site has over 20 categories of questions including Business Travel, Management and Technology.

As on Quora, you can see who has answered each question and click on their profile to learn more about them .

You can answer questions publicly or reply privately . Make sure to provide a useful, topical answer . If you don't, your answer may be flagged as an advertisement or connection-building spam.

LinkedIn Answers has a scoring system called Expertise . Whenever a questioner chooses your answer as the best one, you gain expertise points. With enough points, you will rank on a category's Experts chart.

Getting ranked as an expert will help users interested in that category find you. Don't answer questions just for the points. Contributing helpful information and providing a great answer is the best way to market your business on LinkedIn .

Find Out What Content Your Customers Are Sharing

Your social media content strategy should be driven by the overlap between your expertise and your customers' needs and interests.

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By learning which types of content your customers are sharing, you can create content that will get shared . I'm not suggesting that you copy content. Instead, you should take inspiration from the type and tone of the most shared content .

Are people sharing how-to blog posts, funny memes or instructional videos? Is the most shared content authoritative or friendly and conversational?

With this research, you'll learn what your audience is most interested in sharing and understand the type of content you need to create.

Here are  three places to easily find which types of content are being shared .

Start by browsing all of the tweets for your target keywords. Observe how people are talking about your niche and what they're sharing . Do you see a lot of links? Tweets with photos attached? Remember that Twitter skews towards sharing article links.

You can focus on just the content people are linking to using Twitter's advanced search. Add “filter:links” to the end of your search query. For example, search for “yoga filter:links” to see all tweets containing the word yoga and a link.

Using the Discover tab , you can find the top content among the Twitter users you follow. If you're following users in your target market, you'll see the most popular news articles and blog posts.

The Discover tab is a great way to stay up to date with the latest news in your industry . Being current will help you produce timely, shareable content for your followers . Make sure to comment on this news and offer your perspective.

If you're not already following users in your niche, you can find accounts to follow on WeFollow or Listorious .

Pinterest, the online pinboard, is a great source of visual inspiration. You'll be able to see which types of images are repinned and liked the most . Depending on your topic, you may find photographs, graphics, instructions, cartoons or memes to be the most popular medium.

Then you can create images for Pinterest or other visual social networks like Tumblr or Facebook .

The Pinterest home page will show you the most popular content, regardless of topic. This view is helpful for understanding what works across Pinterest, but it doesn't show you what's best for your niche.

For more specific content, use the Categories menu to view popular pins in the most relevant vertical. Even if a category is too broad for your business, you'll start to see what works for your type of customer .

For the most accurate view, search for your keywords to see the most popular and relevant pins .

Searching for popular pieces of content is more difficult on Facebook than on Twitter or Pinterest. Facebook does offer a search option for public posts, but they aren't sorted by popularity.

Instead, you'll need to go directly to the Facebook pages of your competitors and other businesses, publications and organizations in your industry. You only need to focus on pages with lots of fans because they'll have enough data to see popularity trends.

Browse the top Facebook pages in your niche to see which posts are liked, commented on and shared the most. If you don't know much about the competition, find one or two pages that you do know . Then see which pages they like. You'll find these likes in a box near the top right column of the page's Timeline.

Look closely at the most shared content on 5-10 pages covering the same content as you. Are image posts being shared? Are fans responding to question posts? See which types of content are working best. Then create your own based on what people like .

Social network monitoring is the easiest way to learn more about your target customers . By seeing what questions they're asking and what content they're sharing, you can offer support, establish yourself as an industry expert, define your social media strategy and improve your products .

What do you think? How do you use social media to learn more about your customers? Share your experience in the comments box below.

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How Social Media Influencers Impact Consumer Collectives: An Embeddedness Perspective

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Rebecca Mardon, Hayley Cocker, Kate Daunt, How Social Media Influencers Impact Consumer Collectives: An Embeddedness Perspective, Journal of Consumer Research , Volume 50, Issue 3, October 2023, Pages 617–644, https://doi.org/10.1093/jcr/ucad003

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Research has documented the emergence of embedded entrepreneurs within consumer collectives. This phenomenon is increasingly prevalent as social media enables ordinary consumers to become social media influencers (SMIs), a distinct form of embedded entrepreneur. Whilst research has considered the implications of embeddedness for embedded entrepreneurs themselves, we lack insight into embedded entrepreneurship’s impact on consumer collectives. To address this gap, we draw from a longitudinal, qualitative study of the YouTube beauty community, where SMIs are pervasive. Informed by interactionist role theory, we document the Polanyian “double movement” prompted by the emergence of SMIs within the community. We demonstrate that the economy within the community was initially highly embedded, constrained by behavioral norms linked to established social roles. SMIs’ attempts to disembed the economy created dysfunctional role dynamics that reduced the benefits of participation for non-entrepreneurial community members. This prompted a countermovement whereby SMIs and their followers attempted to re-embed SMIs’ economic activity via role negotiation strategies. Our analysis sheds new light on the negative implications of embedded entrepreneurship for non-entrepreneurial members of consumer collectives, highlights the role of social media platforms in negotiations of embeddedness, and advances wider conversations surrounding the evolution of consumer collectives and the impact of SMIs.

Consumer research has documented the emergence of embedded entrepreneurs within consumer collectives—individuals who leverage their insider knowledge of, and relationships within, the collective to profit from its other members, typically by pursuing entrepreneurial ventures for which they are the primary target market ( Boyaval and Herbert 2018 ; Martin and Schouten 2014 ). This phenomenon is becoming increasingly prevalent as social media platforms, such as YouTube, Instagram, and TikTok, enable ordinary consumers to rise to fame within online consumer collectives and capitalize on this fame by becoming “social media influencers” (SMIs) ( Abidin 2015 ), a distinct form of embedded entrepreneur. Whereas prior accounts of embedded entrepreneurship focus on the development of innovative products and services that address unmet needs within consumer collectives, SMIs’ entrepreneurship typically involves turning their online followers—fellow members of the collectives in which they are embedded—into the target audience for marketing messages in exchange for compensation from brands ( Campbell and Grimm 2019 ). SMIs have emerged within many consumer collectives and are more popular than traditional celebrities amongst younger generations ( Droesch 2020 ). Consequently, the influencer economy is booming, growing from $1.7 billion in 2016 ( Helmore 2021 ) to a projected $16 billion in 2022 ( The Economist 2022 ).

Despite the increasing prevalence of this distinct category of embedded entrepreneur, we lack insight into the consequences of their emergence for the consumer collectives in which they are embedded. Indeed, whilst wider research on embedded entrepreneurship has shed light on the implications of their embeddedness for entrepreneurs themselves ( Boyaval and Herbert 2018 ; Cova and Guercini 2016 ), we have limited knowledge of the impact of embedded entrepreneurship on consumer collectives. Since embeddedness can limit economic activity, embedded entrepreneurs may be tempted to disembed their entrepreneurial activity from constraining social norms within consumer collectives to maximize their financial gains, a phenomenon that theorists of embeddedness argue can have significant social implications ( Polanyi 1944 ). However, despite acknowledgement that embedded entrepreneurship can create tensions within consumer collectives ( Kozinets et al. 2010 ; Scaraboto 2015 ), we lack insight into its implications for the experiences and participation of collectives’ non-entrepreneurial members and for the structure and dynamics of consumer collectives. Given the importance of consumer collectives in consumers’ lives ( Arnould, Arvidsson, and Eckhardt 2021 ), it is important to advance our understanding of embedded entrepreneurship’s social implications. We therefore ask: How does embedded entrepreneurship impact consumer collectives?

We argue that interactionist role theory can shed new light on the implications of embedded entrepreneurship for consumer collectives. From this perspective, shifts in the social roles performed by an individual within a collective—such as the adoption of new commercial roles by an embedded entrepreneur—will inevitably impact those in related counter roles, who may attempt to renegotiate roles within the collective ( Biddle 1979 ). Applying an interactionist role theory lens to a longitudinal, qualitative study of the YouTube beauty community, we document the Polanyian “double movement” ( Polanyi 1944 )—a dual process of disembedding and re-embedding—that occurs as beauty vloggers (video bloggers) within this community become SMIs. We reveal that beauty vloggers’ attempts to disembed by becoming SMIs can create dysfunctional role dynamics that reduce the benefits of community participation for their viewers, as non-entrepreneurial community members. This in turn sparks a countermovement as both viewers and vloggers attempt to re-embed the vlogger’s economic activity by employing a range of role negotiation strategies. However, we demonstrate that the YouTube platform enables SMIs to suppress this countermovement, reducing the extent to which they are required to re-embed their commercial activity, provoking alternative role negotiation strategies with important implications for the community. Our study provides new insight into embedded entrepreneurs’ impact on consumer collectives by revealing the pervasive impact of their role shifts and documenting previously overlooked negative implications for collectives’ non-entrepreneurial members. Furthermore, we illuminate processes of embeddedness negotiation within consumer collectives, highlighting the role of social media platforms in this process, and demonstrating how consumer collectives can evolve as a result of these dynamics. We also extend research on SMIs by elucidating the nature and implications of their embeddedness.

We begin by defining embeddedness and reviewing research on embedded entrepreneurship within consumer collectives, before presenting SMIs as a distinct form of embedded entrepreneur. We then introduce interactionist role theory as a lens that can enrich our understanding of SMIs’ embeddedness and the implications of their entrepreneurship.

Embeddedness, Disembedding, and Re-Embedding

All economies are embedded; entangled in, and thus inseparable from, webs of social relations that shape and limit economic activity ( Granovetter 1985 ; Polanyi 1944 ). In other words, economic exchanges are not driven simply by gain-maximizing self-interest, but also by a desire to form and maintain social relationships by abiding by prevailing social norms ( Granovetter 1985 ; Polanyi 1944 ; Varman and Costa 2008 ). Research has recognized that social norms may manifest as social roles that individuals perform in their relationships with others, with distinct, normative behavioral expectations that must be upheld to avoid tensions and conflict ( Grayson 2007 ; Montgomery 1998 ). Thus, an embeddedness perspective refuses to treat humans as atomized participants in an anonymous market, instead focusing on how their social context influences economic behavior ( Granovetter 1985 ; Polanyi 1944 ), an approach that aligns well with theories of consumer culture ( Kjeldgaard 2017 ).

Not all economies are equally embedded ( Polanyi 1944 ; Granovetter 1985 ). Granovetter (1985 , 491) argues that “ networks of social relations penetrate irregularly and in differing degrees in different sectors of economic life .” Polanyi (1944) famously contrasted highly embedded non-market economies with more disembedded market economies. In non-market economies, economic action is heavily shaped by prevailing social norms and is often dominated by redistribution and reciprocity. In such economies, individuals are largely driven by their obligations to others, whilst self-interested attempts at individual gain are highly frowned upon ( Polanyi 1944 ). In contrast, exchanges in market economies are driven by self-interest and wealth acquisition, largely unencumbered by limiting social norms and obligations ( Polanyi 1944 ). However, Polanyi (1944) argued that, in practice, a truly disembedded market economy cannot exist. Instead, embeddedness exists on a continuum between these two extremes, with economies exhibiting varying degrees of embeddedness ( Polanyi 1944 ). Indeed, even contemporary capitalist economies are never truly disembedded, as evidenced in accounts of commercial friendships ( Price and Arnould 1999 ), moral or gift economies ( Debenedetti et al. 2014 ; Weinberger and Wallendorf 2012 ), and hybrid economies ( Scaraboto 2015 ).

The level of embeddedness within a given social context is not fixed but subject to a process of ongoing negotiation ( Polanyi 1944 ). Since embeddedness constrains economic activity, which must adhere to limiting social norms, profit-maximizing market actors may attempt to “disembed” the economy by severing relational ties or breaking social norms ( Polanyi 1944 ). Such attempted disembedding can have important social consequences, disrupting established hierarchies, norms, and obligations, and removing social protections ( Polanyi 1944 ; Webber 2017 ). It is for this reason that Polanyi (1944) argued that a truly disembedded market economy cannot exist; attempts to disembed always prompt a reactionary “countermovement,” whereby societies strive to protect themselves by “re-embedding” economic activity. This process is referred to by Polanyi (1944) as a “double movement”—a constant back-and-forth between disembedding and re-embedding. Webber (2017) documents a contemporary case of this double movement in his study of English football; initially embedded in local, working-class communities, attempts to impose a “market mentality” through increasing professionalization, commercialization, and marketization ultimately disembedded football clubs from the communities from which they arose. This sparked a countermovement as dissatisfied football fans formed the consumer collective “Against Modern Football” to pursue re-embedding attempts. Webber’s (2017) study demonstrates that Polanyi’s (1944) double movement concept, initially intended to account for societal-level negotiations of embeddedness, can provide valuable insight into the negotiation of embeddedness within consumer collectives. However, we lack insight into the capacity for, and implications of, disembedding attempts performed by embedded entrepreneurs.

Consumer Collectives and Embedded Entrepreneurship

Arnould et al. (2021 , 415) define consumer collectives as “ networks of social relations that arise around consumer goods, brands, other kinds of commercial symbols, and digital platforms .” Consumer research has studied a range of consumer collectives, from subcultures of consumption ( Schouten and McAlexander 1995 ) and consumer tribes ( Cova, Kozinets and Shankar 2007 ) to brand communities ( Muñiz and O’Guinn 2001 ) and brand publics ( Arvidsson and Caliandro 2016 ), each with distinct qualities that have been extensively discussed and contrasted ( Arnould et al. 2021 ). It is widely acknowledged that some members of consumer collectives become “embedded entrepreneurs,” attempting to profit from other members of the collective ( Boyaval and Herbert 2018 ; Martin and Schouten 2014 ; Scaraboto 2015 ). Embedded entrepreneurs initially engage with the collective as an ordinary consumer, without a commercial agenda. However, in accumulating insider knowledge of the collective, they later identify gaps in current market offerings that present entrepreneurial opportunities ( Boyaval and Herbert 2018 ; Cova and Guercini 2016 ). Whilst these individuals have been variously labeled “tribal” ( Goulding and Saren 2007 ), “liquid” ( Biraghi, Gambetti, and Pace 2018 ), and “communal” ( Boyaval and Herbert 2018 ) entrepreneurs, we adopt the term “embedded entrepreneur” ( Martin and Schouten 2014 ; Scaraboto 2015 ), since it is not tied to any single form of consumer collective and neatly captures these individuals’ embeddedness as a result of their membership of, and relationships within, a collective.

Prior research provides insights into the implications of embeddedness for the embedded entrepreneur’s commercial ventures, revealing that consumer collectives can be both supportive and critical. Other members of the collective can encourage and support entrepreneurial efforts by crowdfunding projects, testing prototypes, offering suggestions for product improvement, and promoting the project within their online and offline networks ( Boyaval and Herbert 2018 ; Cova and Guercini 2016 ). However, they may respond negatively when the project does not align with the collective’s philosophy or values ( Boyaval and Herbert 2018 ) or when the embedded entrepreneur prioritizes their own commercial interests over the interests of the collective ( Scaraboto 2015 ). Thus, in line with wider theories of embeddedness ( Granovetter 1985 ; Polanyi 1944 ), embedded entrepreneurs’ economic activity is limited by the collective’s social norms. Embedded entrepreneurs may therefore encourage favorable responses by employing gift-giving narratives ( Scaraboto 2015 ) or involve collective members in product development to foster co-operation and support ( Boyaval and Herbert 2018 ).

Whilst research has explored the implications of their embeddedness for embedded entrepreneurs’ commercial ventures, embedded entrepreneurship’s impact on the experiences and participation of the collective’s non-entrepreneurial members has received less attention. Furthermore, we lack insight into the ways in which a consumer collective may change as a result of embedded entrepreneurship, particularly where it occurs on a large scale or is widespread. Beyond recognition that some members of consumer collectives are resistant to embedded entrepreneurship ( Scaraboto 2015 ), its wider social implications for the collective have been largely overlooked. Brinks and Ibert (2015) note that members may leave a collective when they feel that embedded entrepreneurship alters its dynamics. However, the authors focus on the perspectives of entrepreneurial rather than non-entrepreneurial members of the collective and therefore do not explore these individuals’ motivations for leaving or identify alternative responses to embedded entrepreneurship. Thus, despite growing research into embedded entrepreneurship within consumer collectives, we lack insight into its wider social implications. Yet, profit-maximizing embedded entrepreneurs may be tempted to disembed economic activity from constraining social norms, which may present significant implications for the collectives in which they are embedded ( Polanyi 1944 ). This gap becomes increasingly salient as SMIs emerge as a prevalent form of embedded entrepreneur.

SMIs as Embedded Entrepreneurs

Social media platforms create a “megaphone effect” ( McQuarrie, Miller, and Phillips 2013 ) whereby ordinary consumers can attract large, geographically dispersed audiences through displays of field-specific taste and expertise ( McQuarrie et al. 2013 ; Smith and Fischer 2021 ). Consumers may engage in microcelebrity practices—forms of online self-celebrification that seek to accelerate their rise to fame ( Senft 2013 )—such as self-branding, self-promotion, and intimate self-disclosure ( Abidin 2015 ; Marwick and boyd 2011 ; Senft 2013 ). Whilst few consumers successfully attract and sustain large online followings ( Marwick 2017 ; Smith and Fischer 2021 ), those who do can become celebrities in their own right. Whilst the term “microcelebrity” was initially coined to describe something one does (processes of online self-celebrification) rather than something one is ( Marwick and boyd 2011 ; Senft 2008 ), some scholars use the term to refer to small-scale celebrities within niche online groups ( Marwick 2017 ). However, this usage is problematic since consumers can establish sizable online followings that rival those of traditional celebrities ( Hou 2019 ). Consequently, we instead adopt the term “social media celebrities,” defined as celebrities whose “ fame is native to social media platforms ” ( Hou 2019 , 535). The defining characteristic of social media celebrities is not the scale of their fame but its origin—it does not predate their social media presence but stems from it.

Previous accounts of embedded entrepreneurship typically involve a product or service innovation informed by insider knowledge of the collective’s needs ( Boyaval and Herbert 2018 ; Martin and Schouten 2014 ). However, social media celebrities typically capitalize on their fame by becoming “influencers”—“ individuals who post to their social media accounts in exchange for compensation ” ( Campbell and Grimm 2019 , 110), which may include either monetary fees or non-monetary incentives such as free products and services. Whilst traditional celebrity endorsement typically involves celebrities appearing in brand advertisements on paid media channels targeting a mass audience (e.g., TV and magazine advertisements) ( McCracken 1989 ), influencers post incentivized brand endorsements on their own social media profiles, directly targeting their own followers ( Campbell and Grimm 2019 ). Furthermore, influencers are not simply models, as is typical in celebrity endorsement ( McCracken 1989 ), but are content producers who integrate marketing messages into their social media content in a way that will appeal to their followers ( Campbell and Grimm 2019 ; Kozinets et al. 2010 ).

Traditional celebrities may also become influencers, using their social media presence to generate an additional revenue stream. For instance, footballer Cristiano Ronaldo and reality TV star Kylie Jenner are amongst the highest paid Instagram influencers ( Sweney 2021 ). However, social media celebrities are a distinct form of influencer due to their embeddedness within the consumer collectives that they are targeting with marketing messages. Their origins as ordinary consumers and subsequent rise to fame within consumer collectives mean that social media celebrities typically form strong relationships with other members of the collective through intimate disclosure and online interactions (e.g., “liking” or replying to comments from their followers) ( Abidin 2015 ; Berryman and Kavka 2017 ). Since they are seen as fellow members of the collective, they are bound to uphold its social norms ( Cocker, Mardon, and Daunt 2021 ). Thus, social media celebrities are distinct from traditional celebrities because they are deeply entangled in webs of social relations within online consumer collectives, and this embeddedness shapes the collective’s response to their commercial activities as they become embedded entrepreneurs. In line with existing research, we therefore refer to social media celebrities who become influencers as SMIs ( Abidin 2015 ) to differentiate them from other forms of celebrity influencer.

As in previous accounts of embedded entrepreneurship, SMIs’ embeddedness within consumer collectives may both facilitate and constrain their commercial activity. The feelings of friendship that SMIs' followers experience can motivate them to support SMIs in their entrepreneurial ventures ( Abidin 2015 ; Berryman and Kavka 2017 ), excuse their transgressions ( Cocker et al. 2021 ), and defend them against criticism ( Mardon, Molesworth, and Grigore 2018 ). However, members of the collective may express scepticism and even hostility when SMIs are perceived to prioritize their own commercial interests over those of the collective ( Cocker et al. 2021 ; Kozinets et al. 2010 ; Mardon et al. 2018 ). SMIs may attempt to manage these tensions by ensuring that brand endorsements align with their online identity narratives and organic content, as well as wider norms within the collective ( Cocker et al. 2021 ; Kozinets et al. 2010 ), demonstrating passion and transparency in their entrepreneurial activities ( Audrezet, de Kerviler, and Moulard 2020 ), engaging in emotional labor to shape the collective’s emotional response, and removing negative comments to silence critiques ( Mardon et al. 2018 ).

Whilst prior literature has highlighted the commercial implications of SMIs’ embeddedness, we know surprisingly little about the implications of SMIs’ embedded entrepreneurship for the broader collective. Despite acknowledgement that SMIs’ embedded entrepreneurship can create tensions within consumer collectives ( Cocker et al. 2021 ; Kozinets et al. 2010 ; Mardon et al. 2018 ), we lack insight into how such tensions are experienced by the collective’s non-entrepreneurial members, how they attempt to resolve these tensions, and how the collective might evolve as a result. However, the need to understand the wider implications of embedded entrepreneurship becomes particularly salient in the context of SMIs due to the distinct nature of their entrepreneurship, their fame, and their prevalence.

First, whilst other accounts of embedded entrepreneurship document product or service innovations intended to offer value to the collective by enhancing its focal consumption activity ( Martin and Schouten 2014 ), SMIs target their followers with incentivized marketing messages that can reduce others’ enjoyment of their social media content ( Cocker et al. 2021 ; Kozinets et al. 2010 ). Furthermore, whilst use or purchase of embedded entrepreneurs’ products or services is typically optional, incentivized endorsements constitute an increasing portion of SMIs’ content and disclosures are not always clear ( Cocker et al. 2021 ), making it difficult for consumers to avoid them and increasing their likely impact on the wider collective.

Second, SMIs are deeply embedded due to their fame within the collective. The embedded entrepreneurs previously studied were typically ordinary members of the collective prior to their entrepreneurial ventures ( Martin and Schouten 2014 ; Scaraboto 2015 ) and thus likely had pre-existing relationships with only a select few members of the wider collective. In contrast, SMIs have typically established intimate relationships with a large portion of the collective, who view them as friends ( Abidin 2015 ; Berryman and Kavka 2017 ). Changes to these relationships as SMIs’ fame grows and becomes commodified are therefore likely to have significant implications for the wider collective.

Finally, SMIs are typically highly prevalent within online consumer collectives. Whilst scholars have acknowledged that multiple embedded entrepreneurs can emerge within a consumer collective ( Brinks and Ibert 2015 ), prior research tends to focus on either a single entrepreneurial venture or a small number of distinct entrepreneurial projects ( Martin and Schouten 2014 ). In contrast, multiple members of a single online consumer collective typically become SMIs simultaneously and often engage in highly similar entrepreneurial activities ( Cocker et al. 2021 ; Gannon and Prothero 2018 ; Mardon et al. 2018 ). Such widespread embedded entrepreneurship is likely to have significant implications for the collective.

Thus, SMIs present a distinct form of embedded entrepreneur that highlights the importance of understanding the implications of embedded entrepreneurship for the wider consumer collective. To better understand the nature of SMIs’ embeddedness and the implications of their entrepreneurship, we draw from interactionist role theory.

Understanding Embeddedness through Interactionist Role Theory

Interactionist role theory is not a singular theory, but a set of inter-related perspectives and concepts underpinned by a focus on the performative, relational, and dynamic nature of social roles ( Biddle 1986 ). From this perspective, actors occupy multiple social roles, each associated with distinct, normative behavioral expectations ( Biddle 1986 ; Goffman 1959 ). Roles cannot be performed in isolation since an actor’s role performance is interdependent with the behavior of those in related “counter roles” ( Biddle 1979 ; Sluss, van Dick, and Thompson 2011 ). Consequently, roles are continually negotiated between those in the role and its counter role(/s), and dysfunctional role dynamics may emerge. For instance, the adoption of a social role may force others to take on related counter roles ( Biddle 1979 ), a behavior referred to as “altercasting” ( Weinstein and Deutschberger 1963 ), and actors may experience role captivity when altercast in an undesired role ( Skaff and Pearlin 1992 ). Furthermore, role multiplicity—when an individual occupies multiple roles performed in relation to different but co-present audiences ( Sieber 1974 )—may lead to role conflict, where the behavioral expectations associated with these roles are contradictory ( Ebbers and Wijnberg 2017 ; Van Sell, Brief, and Schuler 1981 ). Actors may attempt to resolve such dysfunctional role dynamics by employing role negotiation strategies, such as redefining or clarifying their roles’ associated behavioral expectations ( Sluss et al. 2011 ), using repudiative tactics to distance themselves from undesired or problematic roles ( Leary 1996 ; Goffman 1959 ), or communicating a role salience hierarchy (i.e., clarifying which roles are considered most important) ( Stryker 1968 ) to diffuse tensions.

Interactionist role theory can enrich our understanding of embedded entrepreneurship and its implications. From this perspective, embedded entrepreneurship occurs when an individual occupies existing social roles within a consumer collective, before subsequently performing additional commercial roles that altercast fellow members of the collective in commercial counter roles. Role theory has previously been used to understand embeddedness in the context of commercial friendships ( Grayson 2007 ; Heide and Wathne 2006 ) but has not been leveraged in studies of embedded entrepreneurship within consumer collectives. Whilst prior research has acknowledged that consumers may occupy various roles within consumer collectives ( Fournier and Lee 2009 ; Leigh, Peters, and Shelton 2006 ; Veloutsou and Black 2020 ) and that they may simultaneously occupy multiple roles ( Martin and Schouten 2014 ; Thomas, Price, and Schau 2013 ), interactionist role theory enables us to advance this work by recognizing and exploring roles’ relationality—that is, the way in which roles are defined in relation to, and negotiated with those performing, connected counter roles ( Biddle 1979 ). This perspective enables us to recognize the dysfunctional role dynamics that can be created by the emergence of embedded entrepreneurs within consumer collectives and to explore how a collective’s members may attempt to negotiate roles to resolve these dynamics. In doing so, this lens equips us to better understand the impact of embedded entrepreneurship on a collective’s non-entrepreneurial members and on the structure and dynamics of the wider collective. To do so, we draw from an immersive, qualitative study of SMIs within a beauty-focused community on YouTube.

Research Context—The YouTube Beauty Community

The YouTube beauty community surrounds beauty-related video content uploaded to the platform by vloggers and watched by other members of the community (viewers). We refer to this consumer collective as a community since previous research has observed key markers of community in this context, such as consciousness of kind, shared rituals and traditions, and a sense of moral responsibility ( Cocker et al. 2021 ; Gannon and Prothero 2018 ). However, as we shall discuss, we found that the community’s characteristics evolved over time. Akin to other, previously documented, consumer collectives ( Schouten and McAlexander 1995 ), the YouTube beauty community has a concentric social structure, consisting of hard-core, soft-core, and peripheral members. Hard-core members included vloggers and those viewers who regularly interacted with other community members by commenting on vloggers’ videos or social media content or by attending offline community events. Soft-core members were passive viewers who rarely interacted with other community members directly but felt that they were part of the community. Peripheral members were viewers who watched vloggers’ videos but did not interact with other community members or feel part of the community.

We selected the YouTube beauty community as our research context for several reasons. First, embedded entrepreneurship was widespread; many beauty vloggers had risen to fame within the community, attracting hundreds of thousands, and even millions, of subscribers to their YouTube channels. As a result of their fame within the YouTube beauty community, many beauty vloggers had become successful SMIs, earning tens of thousands of pounds for a single paid endorsement ( Petter 2019 ). Second, beauty vloggers’ embedded entrepreneurship had created significant tensions within the community ( Cocker et al. 2021 ; Mardon et al. 2018 ), providing an ideal context in which to examine how non-entrepreneurial members of consumer collectives experience and respond to tensions surrounding embedded entrepreneurship. Finally, the research team were deeply immersed in the community prior to the study’s commencement (having watched beauty vlogs regularly since 2012, 2010, and 2014, respectively), which sensitized us to the community’s norms, terminology, history, and evolving structure and enabled us to situate the emergence of embedded entrepreneurship within the community’s broader historical context.

Data Collection and Analysis

We conducted a longitudinal, qualitative study of the YouTube beauty community from 2016 to 2021, using netnography, ethnography, interviews, and archival research ( table 1 ).

OVERVIEW OF DATA SOURCES AND THEIR PURPOSE IN OUR STUDY

Netnography

We employed nonparticipant netnographic observation ( Kozinets 2020 ) to trace shifts in vlogger/viewer roles as the community evolved. Defining a netnographic research field by platform is increasingly problematic due to the fragmented and delocalized nature of many online consumer collectives ( Weijo, Hietanen, and Mattila 2014 ). Indeed, whilst vloggers’ YouTube videos are the community’s primary gathering space, members also interact outside of the YouTube platform, on vloggers’ blogs and Instagram profiles, in discussion forums, and at offline events. Since the research field could not be defined by location, we instead defined its boundaries by selecting 13 focal beauty vloggers (who run 12 YouTube channels, since one channel is operated by a vlogging duo) around which to center our netnography ( table 2 ). We selected UK beauty vloggers that had achieved a notable level of celebrity within the community when the study commenced and whose presence as a vlogger within the community pre-dated the proliferation of influencer marketing activity within this space. We selected only UK vloggers in recognition of location-based differences in regulations and social norms. However, we acknowledge that whilst many of our focal vloggers’ viewers were UK based, others were spread across the globe.

FOCAL BEAUTY VLOGGERS

Vlogger’s primary YouTube channel (some had multiple channels, in which case all were studied).

To the nearest 500,000, as of October 2021.

Earlier videos may have been deleted or hidden from public view by the vlogger.

We studied our focal vloggers’ YouTube, blog, and Instagram content and corresponding comments, capturing data relevant to our study’s research question. We accessed content dating from our focal vloggers’ earliest available posts through to October 2021. Research has highlighted censorship within the YouTube beauty community, with vloggers deleting undesirable comments from their YouTube channels and other social media profiles ( Mardon et al. 2018 ), obscuring more critical viewer perspectives. To capture a wider array of viewer perspectives, we therefore observed two popular discussion forums, each with over 200,000 members, where more critical viewers congregated to discuss vloggers. We read all forum threads posted prior to October 2021 that pertained to our focal vloggers.

Ethnography

We complemented our netnographic data with ethnography, gaining a more complete picture of the community by observing offline interactions between vloggers and their viewers at UK YouTube conventions Summer in the City London (2016, 2017, 2018), BeautyCon London (2016, 2017), and VidCon London (2019), documenting our observations via fieldnotes, photographs, and videos. Attending these conventions over several years enabled us to observe changing vlogger–viewer interactions, whilst attending panel discussions on “creator days” targeting aspiring vloggers provided additional insights into the capacity for market actors such as management agencies, marketers, and social media platforms to influence role dynamics.

Depth Interviews

Between 2018 and 2021, we conducted depth interviews with viewers, exploring their perceptions of our focal beauty vloggers and their broader experiences of the YouTube beauty community. Prospective interviewees completed a screening form and participants were selected purposively to ensure variance in the duration and nature of community engagement, enabling us to capture the views of the soft-core, peripheral, and lapsed community members that were difficult to access via other methods (see table 3 ). We also ensured that all interviewees regularly watched at least one of our focal beauty vloggers, enabling triangulation of data sources. Eighteen interviews were conducted in total, typically lasting between 60 and 90 minutes. Eleven interviews were held face to face and seven were conducted via video call.

INTERVIEW PARTICIPANTS

At the time of interview.

Archival Research

To further capture vloggers’ perspectives, we analyzed podcast interviews with our focal beauty vloggers as well as magazine and newspaper articles, TV shows, and documentaries featuring our focal beauty vloggers. This provided valuable insights into vloggers’ experiences of the role dynamics documented and enabled us to account for the influence of the mainstream media in vlogger–viewer role shifts.

Data Analysis and Interpretation

Consistent with prior research ( Debenedetti et al. 2014 ; Giesler 2008 ), we followed Thompson’s (1997) approach to hermeneutic analysis, iterating between intratextual analysis, intertextual analysis, and comparisons with extant literature. Our intratextual analysis sought to identify the behaviors, behavioral expectations, role negotiation strategies, experiences, and consumer collective characteristics evident in each piece of data (e.g., each video, comment, interview, or podcast episode). Our intertextual analysis sought to identify recurring patterns across the wider dataset. For instance, we identified groups of behavioral expectations that formed specific roles and explored how these roles related to one another and evolved over time, as well as relating these evolving role dynamics to reported experiences of community participation and the changing structure and dynamics of the community. In line with established techniques of hermeneutic analysis ( Thompson 1997 ), we moved back and forth between emergent codes, our data, and extant literature, holding regular meetings to discuss our interpretations until we arrived at a final, holistic interpretation of our dataset.

We begin by providing insight into the nature of vloggers’ embeddedness by documenting their initial performance of social roles within the community, closely interwoven with counter roles performed by its other members. In doing so, we provide context for our subsequent discussion of vloggers’ attempts to disembed by becoming SMIs, which involved enacting new, commercial roles that were inconsistent with behavioral expectations tied to their existing social roles within the community. We demonstrate that these new roles created dysfunctional role dynamics, reducing the benefits of participation for the community’s non-entrepreneurial members. We then document the resultant countermovement, whereby vloggers and viewers attempted to re-embed the SMI’s economic activity via various role negotiation strategies, arriving at a level of role consensus that minimized dysfunctional role dynamics. However, we reveal that this role consensus was achieved in part via countermovement suppression, with vloggers actively censoring viewers’ attempts to further re-embed, resulting in alternative role negotiation strategies with important implications for the community.

Embedding: Initial Social Roles

We found that vloggers and viewers became embedded in the community through the occupation of social roles performed in relation to other community members. Two role dyads initially characterized the community—Guru–Learner and Friend–Friend.

The Guru–Learner Role Dyad

They basically taught me everything I knew about beauty […] I watched tonnes of YouTube videos. Learned how to blend my base, apply liquid eyeliner, the lot. There was a tutorial for pretty much everything. They [beauty vloggers] were really good at explaining things in a simple way, and I’d follow along at home. (Viewer interview, Rhiannon)

I got so many great product recommendations from them in those days. They’d only recommend things that were genuinely good. And if something was shit, they wouldn’t hold back, they’d be like, brutally honest. They warned me off so many crap products. They tried them so we didn’t have to. […] They saved us all a lot of money. (Viewer interview, Lucy)

I used to copy everything Zoe [Sugg, aka Zoella] wore, her hair, her makeup. Looking back now it’s a bit cringey! Zoe got balayage [a hair colouring technique], so I got balayage. Zoe wore a red lip, so I’d try a red lip. […] I learnt a lot of basic makeup skills from them [beauty vloggers], but I also used to watch them just for inspiration, to see what was trendy, see what looks I should try out next. (Viewer interview, Bethan)

Thus, vloggers performed the role of “Guru.” Whilst previous research has acknowledged that community members may adopt a Guru role by serving as a mentor for others ( Leigh et al. 2006 ), the Gurus in our study were expected not only to mentor their viewers by sharing knowledge and transferring skills but also to act as opinion leaders ( Katz and Lazarsfeld 1955 ) by providing unbiased, trustworthy product recommendations, and as tastemakers ( McQuarrie et al. 2013 ) who inspire the consumption choices of others.

Viewer: I have really oily skin & find my makeup is gone by the end of the day, any tips on keeping it on but without caking myself in powder??? Sam Chapman (replying): Maybe you could try using a primer, most of the cosmetic houses sell them now, if you find this still is not working, get an oil free moisturizer. Estee Lauder do a good one ‘day wear oil free’ or mac oil control lotion is good. Go into your nearest counter and ask for a sample before you buy it. Let me know how you get on. (Comments on Sam and Nic Chapman’s YouTube video, January 2009) You used to have actual conversations in the comments back in the day. […] One time a girl was commenting saying she loved the Essie nail varnish in the video but it was out of stock everywhere, so I suggested a dupe [a communal term for a very similar beauty product] […] Another time I commented saying ‘I wish I had hair like hers’ [the vlogger’s] and that I hated my curly hair, and people [other viewers] commented saying ‘same!’ and giving me suggestions for products […] In those days it did feel like we were all helping each other out, like a little community of beauty obsessed weirdos! [laughs]. (Viewer interview, Freya)

In line with prior studies of embeddedness, this role dyad was grounded in trust ( Varman and Costa 2008 ); both viewers and vloggers were expected to provide honest advice and recommendations to their fellow community members, and vloggers in particular were expected to exhibit unwavering honesty due to their trusted Guru position. The trust that underpinned this role dyad was deepened by the equally important Friend–Friend role dyad.

The Friend–Friend Role Dyad

I thought she seemed quite normal […] they seem like just normal people, like your friends, not like celebs, although it's kind of changed now. But it felt like quite a small community back then. […] She was just in her bedroom, talking about products that were quite affordable, high-street stuff. (Viewer interview, Abigail)

I started YouTube because none of my friends were really into beauty that much and I just thought it was a great way to kind of chat about beauty to other people. (Fleur De Force, YouTube video, March 2011) We just enjoyed that process of actually helping people with things that you didn’t have someone to teach you. You know, it was like having a makeup artist in your bedroom to say like this is what you do, this is how you do it. (Nic Chapman, podcast interview, May 2017) It was different back then, they were just ordinary girls who loved beauty, and they were sharing videos because they wanted to, because they liked talking about their favourite products. They weren’t making money out of it, like they are these days, they were just posting because they wanted to. (Viewer interview, Carys)

She just felt like a friend, you know? Like, I watched her wedding video, I know her Mum’s name, her husband’s name, her dogs’ names, I know her favourite foods. I’ve seen every room of her house. It sounds crazy, but I probably knew more about her than I did about some of my friends. (Viewer interview, Lucy)

I just want to thank all of you so much for watching my videos, for all the lovely comments I’ve received over the last two years and for all your support. I just absolutely love making videos for you guys because there’s nothing more exciting to me than when it’s time to upload a video and then I read all your comments coming in, so on that first day I just sit there reading all your comments, replying to you and it just makes my day, it really does … I love you guys. (Tanya Burr, YouTube video, October 2011)

Thus, both vloggers and viewers were embedded within the wider collective due to their occupation of established social roles with distinct behavioral expectations. Whilst an economy existed within the community at this point—for instance, knowledge and performed intimacy were exchanged for attention and status within the community—it was a highly embedded economy ( Polanyi 1944 ), governed by behavioral norms associated with the role dyads documented above. This embeddedness had important implications as vloggers attempted to disembed by pursuing commercial roles that did not adhere to these norms.

Disembedding: Emergent Commercial Roles and Dysfunctional Role Dynamics

Whilst the initial Guru–Learner and Friend–Friend role dyads structured early interactions within the community, two additional role dyads—Influencer–Target Audience and Celebrity–Fan—later emerged. These new, commercial roles represented an attempt by vloggers, and other market actors that encouraged and facilitated the emergence of these roles, to disembed the economy from communal norms to more effectively profit from their trusted position within the community. Consequently, what was once an economy characterized by a reciprocal exchange of resources such as knowledge, intimacy, support, and praise, governed by norms tied to established social roles, became increasingly disembedded as vloggers exhibited the self-interested profit motive that characterizes a more disembedded economy ( Polanyi 1944 ). This attempted disembedding represents the first phase of Polanyi’s (1944) double movement, which he argues has negative social consequences, a claim supported by our findings. Since these commercial role dyads contradicted the behavioral expectations associated with existing social roles within the community, they created dysfunctional role dynamics, and we shall conclude this section by documenting viewers’ experiences of role conflict and role captivity.

The Influencer–Target Audience Role Dyad

Over time the YouTube beauty community was “co-opted” ( Thompson and Coskuner-Balli 2007 ) by marketers wishing to capitalize on vloggers’ trusted Guru role within the community by incentivizing them to promote products and services to their viewers. Brands initially sent vloggers free products and invited them to press events in the hope of generating coverage, whilst vloggers used affiliate links to earn a commission from retailers when their viewers purchased recommended products. As beauty vloggers’ fame and influence within the community grew, brands also began to utilize them in their marketing via paid advertisements, over which they had a level of creative control, and for which they were willing to pay increasingly substantial fees. Between 2010 and 2014, our focal beauty vloggers were signed by talent management agencies who connected them with potential advertisers and negotiated endorsement deals. Consequently, paid endorsements became more commonplace and increasingly lucrative. Vlogger Zoella, for instance, signed to talent management agency Gleam Futures in 2013 and was reported to be earning upwards of £50,000 per month from her online presence by 2014 ( Oppenheim 2016 ). She is now reported to charge upwards of £12,000 for a single Instagram post featuring a brand ( Petter 2019 ). In receiving compensation from brands in return for promoting their products to their viewers, vloggers adopted the role of Influencer. Akin to the bloggers acting as “marketing agents” in Kozinets et al.’s (2010) study, marketing teams expected vloggers acting as Influencers to promote products to their viewers, portraying them in a favorable light.

In adopting this Influencer role, vloggers altercast their viewers in the role of Target Audience; they were expected to watch and engage with the Influencer’s advertising content, and indeed the financial viability of the Influencer role was dependent on them doing so. As the Influencer role grew in prominence, many vloggers left their jobs to become full-time Influencers and thus became dependent on their viewers’ performance of the Target Audience role for income. Thus, resource dependence ( Thomas et al. 2013 ) in the collective changed due to the emergence of the Influencer role; whilst vloggers were initially intrinsically motivated by a passion for beauty consumption and a desire to connect with and help other like-minded consumers, the emergent Influencer role added an additional, financial motivation for community participation. Indeed, in stark contrast to the initial social roles adopted within this consumer collective, the Influencer role involved the self-interested wealth acquisition associated with highly disembedded economies ( Polanyi 1944 ). The mainstream media reported on vloggers’ commercial success (e.g., “ Meet the world’s highest earning beauty influencers ,” Vogue, June 2017), highlighting the presence of the Influencer and Target Audience roles to viewers and the broader public.

The Influencer–Target Audience role dyad shaped the emergence of another, closely related role dyad: that of Celebrity and Fan.

The Celebrity–Fan Role Dyad

Thank you SO much for mentioning me and my gift! […] You are one of my idols and have one of the kindest hearts! I adore you and everything you do and hope to aspire to be half the amazing person you are! (Viewer comment on Tanya Burr’s video, March 2014) The relationship between YouTubers and viewers is changing and how much more excited people are becoming and how there’s this celebrity culture around it […] sometimes I feel like it would be nice to have a bit more privacy, I do have some viewers that know where I live and stand outside . (Zoella, The Creators Documentary Film, March 2015)

ILLUSTRATION OF THE PROFESSIONALIZATION OF BEAUTY VLOGGERS’ VIDEO CONTENT

The Celebrity–Fan role dyad was reinforced at offline community events. Earlier events were smaller and more egalitarian, with vloggers and viewers informally mixing. However, as attendance grew, they became increasingly formalized. At the conventions we attended between 2016 and 2019, vloggers spent most of their time in backstage, “VIP” areas, emerging only for on-stage panel appearances in front of large audiences of viewers ( figure 2 ), or for formal, ticketed “meet-and greets” with viewers ( figure 3 ). Thus, as the community grew, the divide between vloggers and viewers became more pronounced at offline events, perpetuating the roles of Fan and Celebrity.

Vlogger panel featuring Fleur De Force (far left) and Sam and Nic Chapman (center), BeautyCon London 2016

Queues for Vlogger “Meet and Greets,” Summer in the City London 2017

Other market actors also played a role in the celebrification of vloggers. Vloggers’ talent management agencies attempted to amplify their fame (and thus maximize their profitability) by guiding their content creation strategies and microcelebrity practices, as well as securing mainstream media coverage that enforced their Celebrity role (e.g., “ Zoella, Tanya Burr and the UK’s YouTube superstars ,” Telegraph, August 2014). Vloggers began to appear on national TV and on the covers of leading magazines; for instance, in 2015, Zoella was a celebrity contestant on hit TV programme The Great British Bake Off , whilst Tanya Burr graced the cover of British Glamour magazine in the same year. Talent management agencies also encouraged vloggers to outsource content planning, filming, and editing to maximize their productivity, further contributing to the professionalization discussed above and framing vloggers as the “stars” of larger, increasingly outsourced productions.

It started as strictly beauty because that was all I knew. I didn’t think people would want to watch stuff about my life. But as I started getting more viewers and got to know them, I realised they were just interested in me and what I was up to. I found I was passionate about sharing everyday stuff and people could relate to it. So now that’s mainly what I film. (Tanya Burr interview in The Guardian, April 2015)

The emergence of the Celebrity–Fan and Influencer–Target Audience role dyads produced dysfunctional role dynamics in the form of role conflict and role captivity. These dysfunctional role dynamics were experienced most deeply by long-term community members, who were most committed to the role dyads of Guru–Learner and Friend–Friend as performed prior to vloggers’ embedded entrepreneurship. In contrast, those who entered the community following the emergence of the newer, commercial role dyads did not experience the community, and its initial social roles, in their earlier forms and thus did not experience dysfunctional role dynamics to the same degree. We first discuss role conflict, before turning our attention to role captivity.

Role Conflict

I literally can’t remember the last time Zoe [Zoella] posted a proper sit-down makeup tutorial and actually talked about products in any detail. It’s just like a documentary of her life now, and she’ll maybe give a 5 second mention to a beauty product if she feels like it, but that’s it. I mean, maybe that’s what some people want these days, but I feel like… what about me? What about the people who came here for beauty content? (Viewer interview, Freya)

I hate to say this, but I've become less and less interested in YouTube as I've seen the community change. When I first started watching (back in like 2009) it was very community-based, and you felt like you were on the same level as the people you were watching […] Five years on and some of the channels I watched have completely blown up and I'm happy for their success but it's weird and uncomfortable for me. YouTube celebrities get treated more like actual celebrities. It feels less equal and a lot more “us & them .” (Viewer comment on Louise Pentland’s YouTube video, May 2014)

A lot of her videos have someone else filming now, and a lot of the time it's a faceless nameless voiceless person too. It's more like she's a “celebrity” or it's a reality TV show or a talk show. There's someone else there, so it's by default less personal. It feels incredibly contrived. Before she was talking to us through the camera, now she's speaking to a camera held by her appointed camera person. The whole “Look at me, I'm just like you!” doesn't really work anymore. […] I'm not really sure who she is anymore. (Viewer comment on Estée Lalonde’s YouTube video, August 2015)

I feel like it’s less trustworthy now. If they're being paid to promote a brand, you don't really know how entirely truthful they are being about the product. So, like, they could find the lipstick to be not good, it doesn't last long, but because they're getting paid to promote the brand, they can't exactly say it's bad. So, they're going to say ‘yeah, it's really good, I really enjoy the product’, and then it's quite false. So, I don't like that . (Viewer interview, Lauren)

You are NEVER going to give a truly unbiased opinion on the products of a brand if they’ve just whipped you away on a luxury holiday! So I can’t trust you, even if I wanted to. It’s all a big shame. You all gained a following due to your relatability. Not one of you is relatable to me now… (Viewer comment on Patricia Bright’s YouTube video, December 2018)

I just don’t trust their intentions anymore. Like, do they actually still love uploading content, like they claim, or are they just in it for the money? Sometimes I feel like they just see us as their meal tickets. I don’t know whether they really care about us anymore. […] I just don’t feel the same connection to them now that it’s all ads, ads, ads. (Viewer interview, Lucy)

I just felt like I wasn’t really getting much out of it anymore. Like, I can’t really trust their recommendations. Most of them barely even talk about beauty anymore. No-one really talks to each other in the comments. They [vloggers] just ignore your comments entirely. So, you just think to yourself ‘why am I staying?’ (Viewer interview, Carys)

Role Captivity

Quite a few YouTubers talk about their viewers as if we're all incompetent screaming, obsessed little girls. Believe it or not, some of us are civilised beings who watch videos for entertainment purposes and are not obsessed. (Viewer comment on Louise Pentland’s YouTube video, May 2014) I feel like people started to see us as dumb fan girls who worship their [vloggers’] every move and it’s… it’s just a bit embarrassing to be part of. I feel embarrassed and awkward, I don’t want to be seen as an adoring fan, but it feels like that’s the only option you have now if you want to be part of it [the community] . If you comment anything even slightly critical it will either be ignored completely, or you’ll be jumped on by fan girls who call you a ‘hater’ for daring to criticize their idol, or they’ll [the vlogger will] just delete your comment anyway. […] I feel frustrated because it’s like… we made them who they are, we were there from the start, and now it’s like if you’re not willing to be an adoring fan 100% of the time you can get out, see ya! (Viewer interview, Lucy)

When we watch a commercial before the video starts, only to watch a video of an ad, and have a commercial in the middle of the video…it makes [us] turned off to watching the videos. I'm not being mean, I understand this is your job, but we as viewers don't always want to feel like consumers, we are bombarded with it every second of every day. Give us real life. (Viewer comment on Samantha Maria’s YouTube video, June 2017)

It’s frustrating because we [viewers] made them famous, and they’ve used their fame to fill YouTube with adverts, when what we originally liked about the community in the first place was that it was real, genuine opinions from real, genuine people. And we can’t do anything about it. It feels like we either put up with the adverts and act like we don’t mind, or… I mean, there is no ‘or’ really is there? The only options are put up with it or stop watching… [shrugs] […] I don’t know whether I feel like part of the community anymore. […] I want to watch their videos, but I don’t want to be constantly sold to. (Viewer Interview, Keira)

Thus, vloggers’ attempts to disembed produced dysfunctional role dynamics in the form of role conflict and role captivity, which reduced the benefits that many viewers gained from community participation. Next, we explore community members’ attempts to resolve these dysfunctional role dynamics by re-embedding the economy.

Re-Embedding: Role Negotiation as a Countermovement

Polanyi (1944) argued that disembedding attempts are ultimately unsuccessful, as they produce negative societal implications that provoke a countermovement that attempts to re-embed the economy, reinforcing existing social limits on economic action or introducing new limits. Consistent with this claim, we observed that dysfunctional role dynamics motivated a countermovement whereby vloggers and viewers collectively attempted to re-embed the economy by employing role negotiation strategies. Whilst prior research emphasizes how SMIs, and other forms of embedded entrepreneur, attempt to resolve tensions surrounding their entrepreneurial ventures ( Audrezet et al. 2020 ; Boyaval and Herbert 2018 ; Kozinets et al. 2010 ; Scaraboto 2015 ), role theory posits that roles are jointly negotiated ( Biddle 1979 ). With this in mind, we document the role of both embedded entrepreneurs (vloggers) and non-entrepreneurial community members (viewers) in processes of re-embedding via role negotiation. Viewers were motivated to re-embed to minimize the impact of dysfunctional role dynamics on their own participation in a community that provided valuable resources, including information, inspiration, connection with likeminded others, and a sense of belonging. Although vloggers had attempted to disembed to maximize their financial gains from the community, they quickly recognized that they needed to resolve the resultant dysfunctional role dynamics to maintain their role within the community and ensure the longevity of the community itself. We observed four role negotiation strategies employed by vloggers and viewers to re-embed the economy: role distancing , role prioritisation , role reconciliation , and role labelling. Through these strategies, they were able to achieve a level of role consensus within the community—agreement as to how these roles should be performed ( Biddle 1979 )—thus re-embedding by introducing new role expectations that served as social limits to vloggers’ entrepreneurial activities.

Role Distancing

Over the past few years, my life seems to have taken a complete 180. Did I set out for it to become that way? Of course not. Did I ever expect it? Hell no. Did I ever plan on making a living from it? Nope. I was quite set on just making videos to make people happy, regardless of how many people watched them, and that is still my main aim. It just so happens that there is now A LOT of people […] People ask me if I’m a celebrity, and the answer is no. I’m not. I just make videos that lots of people like to watch. (Zoella, blog post, May 2014) The pressure of everything got so much. Like, I was put on this pedestal and reminded daily that I was a role model, and you shouldn’t do that, and you should be saying this, and you should do that, and all those things can feel so suffocating […] I’m a small gal from a little village and all of a sudden it’s just like, ‘whoa this is a lot’ […] I was struggling with the, like, fame stuff. I hate the word fame. (Zoella, YouTube video, April 2017)

It’s not like they set out one day to become these famous vloggers and make a load of money from their viewers. Most of them just started filming videos as a hobby, because they loved beauty, they never expected it would turn into this. (Viewer interview, Bethan) I enjoy your content because I see a woman that never signed up for this fame and success and the seldomly talked about pitfalls that come along with it all. But through it all has managed to stay incredibly grounded and true to herself . (Viewer comment on Zoella’s YouTube video, August 2018)

The influencer word is the one that’s been given a kind of blanket approach to anyone that creates content online, which I completely disagree with. I personally call myself a content creator or a blogger […] those that come under the term influencer are maybe people who have like one channel, their kind of aim is to sell, sell, sell, talk about a new dress or a new top or a new pair of pants every single day to influence people to buy huge amounts and quite often these people have one channel only and that’s kind of like their forte. However, there’s a lot of content creators out there that are very multi-platform. Me, for instance, I’m on Twitter, Pinterest, my blog, my YouTube channel, Instagram, I’m now on Tik Tok […] so for me I’m more of a content creator, I create content continuously for each and every channel, each one is quite different. Also, for me, as well, I started this 7 or 8 years ago before there was any money in it, before there was any hope of any money in it, and it was a hobby, so for me being seen as someone who’s influencing people isn’t really the way I like to see it. (Inthefrow, YouTube video, April 2020)

I think there’s a big difference between a content creator and an influencer. Influencers tend to come across as though they are just doing a job/making money rather than like you say creating something that people want to see and would be interested in buying alongside great content (Viewer comment on Inthefrow’s YouTube video, April 2020) They’re not your standard Love Island [reality TV programme whose contestants are known for becoming influencers following their appearance on the show] influencers who’ll just accept brand deals for anything that comes their way. I feel like it’s not fair to put them in the same category. They [vloggers] have built up an audience from scratch and they know that they need to be picky about what ads they do, they won’t just advertise anything. You can tell they value their audience much more than other types of influencer. They’re not going to sell them anything they don’t actually like. […] I would trust their recommendations, whereas I wouldn’t trust a Love Island influencer’s ad. (Viewer interview, Jessica)

Reiterating vloggers’ role distancing narratives surrounding the Influencer role enabled viewers to reduce their own experiences of role captivity, by distancing themselves from the Target Audience role, and reduce role conflict by perpetuating the view that the Influencer role, as adopted by vloggers, was not as contrary to the Guru role as one might imagine.

Role Prioritization

I’m going to be completely honest here […] I wanna be completely transparent […] I’ve been using the Wild deodorants ever since they sent them to me as a gift, but I’ll be completely honest here now when I say I have stopped using them as of last week because the white marks were too much for me, too much. The black items that I wear and my black bras are covered, and I mean covered in white stains from these and it was getting too much […] I feel awful saying it. (Inthefrow, YouTube video, October 2021)

Saying the reality of a product as it is without sugar coating it for the sake of viewers/consumers who go out and spend their hard-earned money on products, is called integrity and is very appreciated. Thank you Victoria. (Viewer comment on Inthefrow’s YouTube video, October 2021)

You are kind of in the middle […] I am always thinking ‘are the brand going to get what they want out of this’, but I’m also… my priority is that my audience are happy […] [I’m] just trying to constantly educate brands on little things like that and how this isn’t an advert, I can’t follow a script, it has to be natural, in my own voice […] it’s just about being honest and over the years I have turned down so many amazing jobs moneywise […] I probably turn down 8 out of 10 jobs that I get [offered] […] I would never risk everything just for a bit of money or whatever to work with a brand. (Lily Pebbles, podcast interview, April 2018)

Fleur seems to be making enough money to be able to pick and choose sponsorships. I bet there's many that she's refused because she does not like the product (Viewer comment on Fleur De Force’s video, August 2017) I was definitely concerned when they first started doing ads. I was thinking ‘how can you give an honest opinion if they’re paying you?’ But I don’t worry about it now. […] They’re really good at putting their viewers first. They turn down so many opportunities, they don’t do ads for any products they don’t actually like, they’re really selective. So, I do still trust their advice. (Viewer interview, Bethan)

We also observed vloggers going “back to basics,” reviving old techniques such as filming on more basic cameras or on their mobile phones, without a carefully staged and professionally lit “backdrop.” For instance, Tanya Burr promoted a new makeup range whilst sitting on her hotel room floor wearing her dressing gown, after telling her viewers that she had “ just set up a very makeshift tripod on some boxes that don’t look very stable! ” ( figure 4 ). Similarly, Fleur De Force informed her audience: “ I’m actually filming on my vlog camera which is a little bit different […] it’s like less high quality but a little bit more on the fly and obviously a lot less zoomed in, you can see the room behind me and stuff ” ( figure 4 ).

DE-PROFESSIONALIZATION OF BEAUTY VLOGGERS’ VIDEO CONTENT (LEFT: TANYA BURR, 2017 RIGHT: FLEUR DE FORCE, 2018)

Lily Pebbles: I haven’t touched my big SLR camera or studio lights for over a year now and I’m thinking of just getting rid of them, it’s just weird. Tanya Burr: Yeah, well we don’t have any in the house now, we have some at the office space, but I definitely like vlogging more now and I find it hard to find a reason to do a really like highly produced video because I think I want to sit and chat to my audience… The Anna Edit: It’s nice, it feels more personal and I feel like we’re all making content that we watch. It got to the point where I was like, I don’t really tend to watch 3-minute look book videos, I want like a 20-minute, 25-minute vlog, sit-down chat where you feel like you’re getting to know your friends and I think it’s interesting how it’s kind of done a full circle, back to almost where we started. (Tanya Burr interviewed on Lily Pebbles and The Anna Edit’s podcast, July 2018)

The three vloggers described how they had reverted to earlier filming styles and production values. In doing so, the vloggers prioritized the earlier Friend role, and viewers’ expectations of relatability and intimacy, over the Influencer role and marketers’ expectations of high-quality, professional content. Vloggers thus attempted to resolve dysfunctional role dynamics by demonstrating loyalty to the collective, prioritizing the needs of its members over their own commercial interests and the interests of the marketer. This role prioritization strategy reassured many viewers that, should conflicts arise between their multiple roles, vloggers would prioritize the Friend and Guru roles, thus avoiding negative implications for the community.

Role Reconciliation

Role reconciliation involved vloggers reconciling the demands and expectations of their earlier community roles with those of their emergent, commercial roles. In role theory, role enrichment refers to instances whereby the performance of one role improves the performance of another role ( Greenhaus and Powell 2006 ). Whilst the new roles of Celebrity and Influencer do not at first glance appear to enrich the roles of Friend and Guru, and indeed create role conflict as documented above, role reconciliation involved vloggers identifying and highlighting ways in which their new, commercial roles could enrich the initial roles of Friend and Guru that were so integral to the community.

Today is the day that I’m allowed to reveal to you guys the cover and show you on this vlog […] I hope you guys enjoyed seeing like behind-the-scenes on the shoot and, like, how the pictures were created. […] I organised a competition with Glamour because I said I really wanted to go for afternoon tea with my viewers, I thought that would be such a cute idea. So, all you have to do [to enter] is rip out this page. (Tanya Burr, YouTube video, October 2015)

I get to work with some of the most prestigious and established brands in fashion, beauty and beyond by teaming up alongside them to launch, celebrate and promote their products and causes. I also spend a huge amount of time creating organic content like so many of the editorial blog posts, YouTube videos and Instagram creations you see on a weekly basis, and the paid campaigns I take part in allow me to continue to produce so much organic content in-between. (Inthefrow, blog post, February 2021)

The whole point of me being an ambassador really is to kind of help you guys hear about new products, new product launches, things I’m loving the most. You know, because a lot of you guys follow me because you like what I recommend and I guess as an ambassador in this way it's just a nice way for me to kind of be able to talk to you guys about what I’m enjoying, what I’m using all the time, new product launches that I think you’ll be interested in. (Inthefrow, YouTube video, January 2018)

I’m, you know, lucky enough to be a YouTuber, I’m so privileged to receive items like this. I did a huge giveaway in February and I’ve picked six winners who have all been DM’d [direct messaged] […] As much as I can try to give back, I do. (Patricia Bright, YouTube video, March 2018)

I appreciate it when she [Inthefrow] does giveaways and free samples, things like that. I think it’s nice that she’s using her position to benefit us, giving something back to her viewers. It’s like a way of saying ‘I appreciate you’. (Viewer interview, Sophie) I personally like the ads because part of the reason I'm following you is to learn about what products are out there because I certainly don't have time! I also like that you're making ad money so that you can continue to post free content! :) (Viewer comment on Estée Lalonde’s video, February 2019)

Role Labeling

Role labeling strategies involved vloggers clearly labeling role transitions, clarifying when they were and were not performing the Influencer role. Role transitions refer to movements between roles ( Ashforth 2000 ) and can involve both macro role transitions (changes between sequentially held roles) and micro role transitions (movements between simultaneously held roles). Vloggers regularly engaged in micro role transitions between their established Guru role (whereby they provided honest and unbiased opinions) and their Influencer role (whereby their recommendations were biased by incentives received from brands, who expected positive coverage). Vloggers’ micro role transitions to the Influencer role were initially obscured from viewers; vloggers referred to content vaguely as “supported by” certain brands, thanked a brand for “making this video possible,” or simply omitted mention of brand involvement entirely. However, some viewers commented on vloggers’ video content accusing it of being an undisclosed advertisement and/or asking for clarification on brand involvement. Indeed, a small number of viewers reported vloggers’ content to the UK’s Advertising Standards Authority (ASA). Whilst there were no specific guidelines for influencer marketing on social media at this time, several vloggers’ posts were subsequently banned by the ASA for failing to abide by the regulator’s general advertising code, which stated that advertisements “ must be obviously identifiable as such. ”

I love how you put (ad) in the title of the video and not try to play it off like most beauty gurus. Love you lots girl! (Viewer comment on Estée Lalonde’s video, April 2015) Viewer: thanks Fleur, for being transparent and following the rules. I’ve been watching you since nearly the beginning of your channel, I trust that you’re giving an honest opinion and that you’re working with brands that you respect and like… There is a large group of your viewers who recognize your integrity, let their voices be louder.  Fleur (replying): Thank you for your sweet comment … I always try my absolute best to follow the rules and disclose properly, and 100% believe in being totally truthful for you guys :) xo (Comments on Fleur De Force’s video, June 2017)

Viewer: This video should have AD in the title since there was items and a trip gifted by a brand.  Inthefrow (replying): According to the new CMA guidelines AD should only be used if there was payment from a brand (this can be financial or PR gifts) AND creative control over the content. I have checked this is correct and they have informed me that their guidelines are ‘not an either/ or’ there HAS to be both creative control and payment (in either form) for AD to be used :) This is how you clarify an advertorial from organic uncontrolled content, and this has had no brand control over any part of it x (Comments on Inthefrow’s YouTube video, February 2019)

I think it’s much easier now they have to write AD on the posts, because then you take it with a pinch of salt. It’s fine as long as they make it clear it’s an ad. They get to make money and support themselves, but we know not to take that video, or that recommendation, as seriously. […] I wouldn’t be rushing out to buy it if they’ve mentioned it in an ad video, because I know they’re only mentioning it because they’ve been paid to. I’d pay more attention to things they recommend in the videos that aren’t ads, because I know that’s their genuine opinion. So, as long as it’s clear which one’s which, I don’t mind, and most of them [vloggers] are pretty good at including ‘ad’ on their posts these days. (Viewer interview, Charlotte)

To summarize, dysfunctional role dynamics motivated a countermovement whereby vloggers and viewers jointly attempted to re-embed the economy using role negotiation strategies that introduced new norms surrounding when and how various roles should be performed. This created a level of role consensus that minimized dysfunctional role dynamics within the community. However, as we shall discuss below, this role consensus was achieved, in part, by suppressing the countermovement, silencing those who sought to further re-embed.

Countermovement Suppression and Alternative Role Negotiation Strategies

Role negotiation typically involves a back and forth between actors as they try to agree upon acceptable role enactments; however, actors’ capacity to negotiate roles can be constrained by societal norms, or by an actor’s limited social status ( Biddle 1979 ). In this case, we found that platform affordances—specifically the comment moderation function on YouTube and other social media sites, which enabled vloggers to delete or block viewer comments—significantly limited viewers’ capacity to negotiate community roles. Whilst YouTube’s comment moderation tools empowered vloggers by giving them greater control over their YouTube channels, these tools simultaneously disempowered viewers by limiting their ability to have their voice heard in the community’s central gathering space ( Kozinets et al. 2021 ), thus restricting their capacity to negotiate community roles through open discussion with other community members. Some viewers, in particular long-term community members who were highly committed to the community’s initial role dyads, felt that further re-embedding was necessary but found that their attempts to initiate further role negotiation were silenced as their comments were blocked or deleted. In other words, vloggers were able to achieve role consensus within the community in part by suppressing the countermovement. Some viewers therefore attempted to resolve dysfunctional role dynamics through alternative approaches that involved altering their participation in the community.

Role Distancing via Community Disengagement

I do watch her videos from time to time, but not religiously like I used to. Nowhere close. Literally like, maybe once a year I might go on YouTube and see what everyone’s up to, whereas I used to watch their videos every single day without fail! And I never comment anymore. I’d say I haven’t commented for maybe, 5 years, if not longer. […] I wouldn’t say I’m part of the community in the way that I was. I’m just a nosy person who pops back in every now and again, has a quick look around, and leaves again. It’s not for me anymore, I don’t feel like I’m a part of it. (Viewer interview, Lucy)

I remember, back in the day, we could put up a beauty tutorial and it would get 500,000 views in a day. Those days have come and gone my friend […] well they’ve come and gone for me at least. (Estée Lalonde on her own podcast, April 2019)

I don’t click on their links, like their affiliate links under the video on YouTube or the swipe ups on Instagram. I always make a note of the product name and then I search for it on Google instead. I just don’t personally agree with them [vloggers] making money off us [viewers]. It makes me feel uncomfortable. […] Searching for products myself makes me feel like I’m more, like, in control. I can take their recommendations without giving them money. I feel more comfortable with that. I can’t stop them posting affiliate links, but I can choose not to click on them. (Viewer interview, Rhiannon)

I haven’t watched them [beauty vloggers] for over a year now. I just didn’t feel I was getting anything from the videos anymore, it felt like they weren’t for people like me. I used to get all these great recommendations, actually learn something new, whereas now they only talk about products that they’re paid to talk about. It does feel a shame. […] If we could go back in time to the old days I would 100% go back. I would love it. I miss those days when it was a little close-knit community, and it felt like they [vloggers] actually cared about us, they were one of us. But it’s not like that anymore, and I’m not going to stay just to be sold to at every opportunity . (Viewer interview, Freya)

These viewers were not actively trying to re-embed vloggers’ economic activity through their role distancing strategies. Instead, their perceived powerlessness in role negotiation led them to distance themselves from the undesired roles of Fan and Target Audience via various levels of disengagement from the community.

Role Inversion and Re-Embedding via anti-Fan Communities

Whilst some viewers simply distanced themselves from the Fan and Target Audience roles, others instead rejected these undesirable roles by creating a new role for themselves—the Anti-Fan. This role was an inversion of the Fan role that many felt had been forced upon them and involved engaging in online critiques of vloggers and attempts to further re-embed their economic activity.

I was so hoping people would call her out for that in the comments, but it’s just full of sycophants! Do they not realise that deleting negative comments is a lot of the reason forums like this exist? Because normal people can’t stand echo chambers! (Forum post, February 2020)

I hate this overly positive-to-the-point-of-delusion, back-off-haters attitude we are supposed to have these days where any level of critical thinking or reflection is seen as “bad vibes.” […] These crazy fan girls who praise these vloggers for the bare minimum of effort and the vloggers that lap it up are stunting the growth of this sector of YouTube and turning it vapid, and us here who have valid opinions about the morality and honesty of these vloggers are haters and get deleted. Yay. (Forum post, October 2018)

Viewers described being dismissed by vloggers and other viewers as “haters” for posting video comments offering what they perceived to be constructive feedback intended to help the vlogger perform their commercial roles in a manner more acceptable to the wider community—in other words, for their attempts to re-embed. The anti-fan community therefore presented an opportunity to escape from the Fan role by acting instead as an Anti-Fan, critical of the emergent role dyads.

Is it just me or were all the questions Zoe [Zoella] was allegedly sent in for her [Instagram] story just all the recent comments we've called out about her over the past couple of weeks  Reply: there’s no way people sent her those questions! It’s like you say, she just responded to all the posts on here!           (Forum posts, August 2020) Popping in to point out that since we discussed her low blog traffic and how she doesn't even promote articles to her audience, she HAS started sharing them on Instagram stories. So to the super tiny Inthefrow team reading this thread, you're welcome for the common sense advice! (Forum post, June 2021)

I’m going to report every vlog that’s not disclosed to be an AD, if you guys have time, you should do the same. It’s the only way to teach her, take the money away and maybe she will wake up. (Forum post, October 2018) They [the CMA] don't open cases on behalf of individuals so I won't get any updates on this particular case. I get the impression they just keep a bank of these complaints so it's worth posting on here to bitch and moan and then sending a quick link to the offending post to their general enquiries email address at the same time - make it your routine and maybe our small little community can enact some change. (Forum post, Sept 2019)

When Embedded Entrepreneurship Disembeds: Implications for Non-Entrepreneurial Members of Consumer Collectives

Our interactionist role theory lens provides new insights into the nature and implications of embeddedness in cases of embedded entrepreneurship. We have demonstrated that embeddedness can involve the occupation of social roles within a consumer collective, with associated behavioral expectations that impose limits on economic activity. Existing research has acknowledged that embeddedness can involve the occupation of social roles ( Grayson 2007 ; Montgomery 1998 ). However, we examine this form of embeddedness in the context of a consumer collective and demonstrate that interactionist role theory can shed new light on processes of disembedding and their implications. In our research context, embedded entrepreneurship involved attempts to disembed ( Polanyi 1944 ) as vloggers adopted new, commercial roles that contradicted behavioral expectations associated with their existing roles, thus deviating from social norms in the pursuit of personal gain. It is important to note that embedded entrepreneurship does not inherently involve disembedding. Depending on the nature of an individual’s embeddedness in the collective (i.e., the social roles they currently occupy, their associated behavioral expectations, and connected counter roles) and the nature of their entrepreneurship (i.e., the new commercial roles that they adopt, associated behavioral expectations, and connected counter roles), contradictions in roles’ associated behavioral expectations may or may not occur. However, our study provides insight into the social implications of embedded entrepreneurship that does involve disembedding attempts.

We demonstrate that embedded entrepreneurs’ attempts to disembed can create dysfunctional role dynamics. Whilst previous research has identified a range of isolated social roles within consumer collectives ( Fournier and Lee 2009 ; Leigh et al. 2006 ; Veloutsou and Black 2020 ), we extend this work by demonstrating that embedded entrepreneurs’ role shifts can have important implications for the roles performed by non-entrepreneurial members of a collective. We reveal that role multiplicity emerging as a result of embedded entrepreneurship can produce role conflict that can negatively impact the entrepreneur’s performance of their pre-existing social roles within the collective and render non-entrepreneurial members unable to satisfactorily enact their own desired roles. Furthermore, we recognize the capacity for embedded entrepreneurs’ role shifts to altercast other, non-entrepreneurial members of a collective into new roles. We reveal that, in such instances, the collective’s non-entrepreneurial members may experience role captivity. As we discuss further below, these feelings of role captivity are amplified when non-entrepreneurial members’ capacity to negotiate these roles is restricted. These findings provide new insights into the implications of embedded entrepreneurship for non-entrepreneurial members of consumer collectives, shedding light on these individuals’ experiences and demonstrating the capacity for embedded entrepreneurship to reduce the benefits they perceive in participating in a consumer collective.

In doing so, our study helps us to understand consumer collectives’ resistance to embedded entrepreneurship. Prior research acknowledges that tensions emerge when embedded entrepreneurs prioritize their own commercial interests over the needs or values of the collective ( Boyaval and Herbert 2018 ; Scaraboto 2015 ), an observation supported by our findings. However, we have shown that embedded entrepreneurship can not only create issues surrounding the embedded entrepreneur’s own perceived trustworthiness and loyalty to the collective but can also reduce the benefits other members gain from participation. Here, we see an illustration of the “enactment tensions” discussed by Thomas et al. (2013) whereby tensions arise as heterogeneous community members engage with the collective in divergent ways that impact the identity enactments of other members. Whilst Thomas et al. examined tensions that occur when new and more diverse members enter a consumer collective, we show that enactment tensions can also arise when an existing member of the collective undergoes a significant role shift that impacts the roles performed by its other members.

Embeddedness Negotiation and Disempowerment in Consumer Collectives

Our analysis provides insight into evolving levels of embeddedness within consumer collectives, highlighting the capacity for role shifts and role negotiation to contribute to a wider process of embeddedness negotiation. Consistent with Polanyi’s (1944) conceptualization of the double movement, we have shown that embedded entrepreneurs’ attempts to disembed are unsustainable, met with a protective countermovement that attempts to re-embed the economy via role negotiation. Previous research has acknowledged that embedded entrepreneurs, including SMIs, may attempt to resolve tensions surrounding their entrepreneurial ventures ( Kozinets et al. 2010 ; Scaraboto 2015 ); however, roles cannot be negotiated in isolation ( Biddle 1979 ). We therefore extend prior work by documenting a relational process of role negotiation performed by both embedded entrepreneurs and non-entrepreneurial members of consumer collectives, with both parties motivated by the resource dependence discussed by Thomas et al. (2013) . We show that these role negotiation strategies enable the community to re-embed the embedded entrepreneur’s economic activity, reaching an apparent consensus surrounding behavioral expectations relating to their new, commercial roles. However, whilst a degree of consensus appears to have been reached within the community, we have shown that this is in part due to the suppression of the countermovement. SMIs re-embedded to the level required to satisfy a sufficient portion of the collective to maintain their audience and thus their commercial appeal for brands. However, they silenced the more vocal and critical minority who felt that further re-embedding was necessary. We reveal that the YouTube platform, and other social media platforms, played an important part in countermovement suppression, preventing the fulfillment of the double movement as described by Polanyi (1944) .

Recent consumer research has drawn attention to the constraining effects of platform affordances ( Kozinets et al. 2021 ) and scholars have called for more research on the role of platforms in shaping interactions within consumer collectives ( Dalli 2021 ). Our study responds to this call by highlighting the capacity for social media platforms’ affordances to influence community members’ power over social roles within the collective, and thus over the structure and dynamics of the collective. Our study shows that the voice-based power ( Kozinets et al. 2021 ) of non-entrepreneurial community members is restricted by platform affordances, which enable vloggers to limit the types of opinions that can be vocalized within the community. Whilst prior research has acknowledged SMIs’ censorship of critical opinions ( Mardon et al. 2018 ), we reveal previously unrecognized implications for role negotiation within the collective. We found that some viewers were unable to attempt to negotiate community roles on the YouTube platform itself, motivating them to employ alternative role negotiation strategies with distinct implications for the community. We found that non-entrepreneurial community members engaged in distinct forms of role distancing that involved disengaging, to varying degrees, from the community. Whilst Thomas et al. (2013) propose that resource dependence can motivate community members to strive to maintain community continuity, we show that, where dysfunctional role dynamics erode the benefits of community participation, this resource dependence may no longer be sufficient to retain all members of the collective. Alternatively, they may adopt a role inversion strategy, forming related anti-fan communities that enable them to attempt to further re-negotiate roles and thus re-embed the embedded entrepreneur’s economic activity. Despite anti-fan community members’ claims that vloggers were reading their posts, the anti-fan communities appeared to have limited impact upon roles within the central community; however, anti-fan community participation enabled these individuals to attempt to regain the voice-based power that they lacked on the main YouTube platform.

Embedded Entrepreneurship and the Evolution of Consumer Collectives

Our research highlights the capacity for embedded entrepreneurship to influence the characteristics of a consumer collective, thus shaping its evolution. For instance, whilst prior research proposes that consumer collectives tend to focus on a specific brand, consumption activity, or consumption ideology ( Thomas et al. 2013 ), we found that the focus of the community changed over time from a consumption activity (beauty consumption) to a series of individual brands (SMIs themselves as celebrity brands). Consequently, what began as a united community of practice ( Wenger et al. 2002 ) transformed into a series of related brand communities. Furthermore, we show how interaction within a consumer collective can change over time. In its early days as a community of practice, communication between viewers and vloggers, and between viewers themselves, was commonplace, with interactions typically centered around beauty consumption. As a result of role shifts surrounding embedded entrepreneurship, interaction between viewers became less common, and viewer comments directed toward vloggers resembled fan interactions. As some interaction remains, the collective cannot be classed as a brand public ( Arvidsson and Caliandro, 2016 ). Instead, the community increasingly resembled the hubs described by Fournier and Lee (2009) , lacking the viewer-to-viewer interactions that are key in contributing to community belonging. McQuarrie et al. (2013) noted that communities become less communitarian as SMIs emerge and our findings extend their work by providing insights into how and why this can happen.

We also shed new light on consumers’ motivations for leaving consumer collectives. Prior research identifies a range of motivations for leaving, such as the growing costs of participation ( Seregina and Weijo 2017 ) and disillusionment due to the introduction of disruptive market logics ( McAlexander et al. 2014 ). Extending this work, we show that dysfunctional role dynamics can erode the benefits consumers gain from the community, thus reducing their motivation to engage in role negotiation strategies that would enable them to continue their participation. Furthermore, we extend extant research that recognizes the capacity for consumer collectives to give rise to new, related collectives, and provide new insights into motivations for participating in these collectives. McAlexander et al. (2014) have shown that those that leave consumer collectives may form oppositional collectives, which provide an opportunity for connection with other lapsed members and sensemaking surrounding their choice to leave. We show that such oppositional collectives may also emerge as a result of a suppressed countermovement in the collective’s central gathering space and may offer an alternative space whereby community members can attempt to further re-embed the economy and regain control over role negotiation, thus presenting new ways to escape role captivity and to attempt to resolve role conflict. We also offer insight into the relationship between these oppositional collectives and the collectives that they are positioned in opposition to. Like the oppositional collectives studied by McAlexander et al. (2014) , the anti-fan communities studied were positioned relative to the original community. However, unlike the oppositional collectives in McAlexander et al.’s study, these anti-fan communities continued to engage with, and attempted to shape, the original collective.

Our study contributes to the growing, interdisciplinary literature on SMIs. Prior research highlights the benefits of SMIs’ embeddedness, observing that their followers’ trust in, and parasocial relationships with, SMIs can make them successful endorsers ( Breves et al. 2021 ). However, our study reveals that SMIs’ embeddedness also limits their economic activity as SMIs must abide by normative expectations associated with their existing social roles within the consumer collectives in which they are embedded. Failing to uphold these norms—that is, disembedding—creates dysfunctional role dynamics that prompt a countermovement, requiring the SMI to attempt to re-embed their economic activity through role negotiation. Whilst research has provided insight into the emotional ( Mardon et al. 2018 ), attention ( Brooks, Drenten, and Piskorski 2021 ), and visibility labor ( Abidin 2021 ) undertaken by SMIs to acquire and monetize their audience, we extend this work by revealing the skilled role negotiation required for SMIs to profit from their audience in an enduring and sustainable way.

Furthermore, whilst prior research has explored the value consumers derive from SMIs ( Scholz 2021 ), our study reveals the negative implications of SMI’s emergence and evolution for the individuals who follow them. Our study reveals that SMIs can disrupt consumers’ enjoyment of consumer collectives that were once important to them, potentially forcing them to disengage from or leave the collective. Our research also contributes to existing work in media studies on SMIs and anti-fandom. Prior research acknowledges that anti-fan communities provide a space for consumers to critique SMIs that they perceive as lacking in authenticity ( Duffy et al. 2022 ). However, we show that consumers may also participate in SMI anti-fan communities when countermovement suppression within another collective prompts them to seek an alternative space where they can attempt to further re-embed its economy. In doing so, we account for the prevalence of anti-fan communities in the context of SMIs.

Future Research Directions

Whilst we studied a single consumer collective with distinct characteristics, the role dynamics described are unlikely to be unique to this context. SMIs have emerged within a variety of consumer collectives on a range of social media platforms. The Instagram cleaning collective and the BookTok collective on TikTok are just a couple of consumer collectives in which social media content creators and their followers have experienced role shifts that draw parallels with those documented in our study, and we anticipate that similar role dynamics would therefore emerge in these contexts. However, variations may occur. For instance, members of brand publics may be less averse to these role shifts since they are typically less invested in the collective than members of consumption or brand communities ( Arvidsson and Caliandro 2016 ). Furthermore, distinct comment moderation mechanisms across various social media platforms may result in different role negotiation strategies becoming prevalent, with implications for the collective’s evolution. Research should explore how the characteristics of consumer collectives and the affordances of social media platforms influence role dynamics surrounding SMIs’ emergence.

An important question raised by our study is whether similar role dynamics would emerge surrounding embedded entrepreneurs that are not SMIs. Dysfunctional role dynamics are particularly prevalent in the context of SMIs because they develop close relationships with a mass audience within the collective, and therefore, their subsequent role shifts disrupt many role relationships. Whilst members of offline consumer collectives can achieve celebrity status within the collective without the use of social media ( Thornton 1995 ), this occurs less frequently as it is much harder for ordinary consumers to rise to fame without the aid of social media’s “megaphone effect” ( McQuarrie et al. 2013 ). Where members of consumer collectives are only known by a small number of fellow members, their embedded entrepreneurship is less likely to create significant role dysfunction. However, many consumer collectives exhibit a hybrid form; even collectives that regularly meet offline also have online gathering spaces where members may use the megaphone effect to rise to fame ( Seregina and Weijo 2017 ). Future research should explore the implications of this hybrid nature; how might role dynamics differ in consumer collectives that exist largely offline? What role negotiation strategies emerge in offline spaces, and how do they differ from those enacted in online spaces?

Our findings are not directly applicable to other forms of celebrity influencer. Whilst many traditional celebrities also act as influencers, consumers may respond differently to their enactment of this role due to differences in the origins of their fame. Social media celebrities exhibit a distinct form of embeddedness since they occupy existing social roles within online consumer collectives prior to the occupation of Celebrity and Influencer roles. This is not typically the case for celebrities whose fame pre-dates their relationships with their online followers. This is not to say that other types of celebrity cannot become embedded. Indeed, traditional celebrities use social media to cultivate more intimate relationships with their audiences ( Marwick and boyd 2011 ). Thus, just as work on commercial friendships has considered both instances in which friendships become commercial relationships ( Grayson 2007 ), and where commercial relationships become friendships ( Price and Arnould 1999 ), there is value in exploring how traditional celebrities may become embedded through their social media use and unpacking the implications this may have on their role as an influencer.

Finally, we have shown that social media platforms may grant some individuals more power than others in shaping their role within, and indeed the wider structure of, online consumer collectives. Given the importance of these collectives in consumers’ lives, this unequal power distribution warrants further investigation. Research should explore the implications for consumers’ identity projects when their limited capacity to negotiate their role within online consumer collectives forces them to distance themselves from or leave collectives that were previously important to them, as was the case for many consumers in our study.

All authors collected the netnographic and archival data between 2016 and 2021. All authors conducted ethnographic research at Summer in the City London in 2017 and 2018 and at BeautyCon London in 2017. The second author also collected ethnographic data at Summer in the City London in 2016, at BeautyCon London in 2016, and at VidCon London in 2019. Interviews were conducted between 2018 and 2021 by the first author and a research assistant. All authors jointly analyzed the data. All data are currently stored on OneDrive and the Open Science Framework under the management of the first author and are accessible to all authors.

Rebecca Mardon ( [email protected] ) is a senior lecturer in marketing at Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK.

Hayley Cocker ( [email protected] ) is a senior lecturer in marketing at Lancaster University Management School, Lancaster University, Lancaster LA1 4YX, UK.

Kate Daunt ( [email protected] ) is a professor of marketing at Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK.

The authors are grateful for the Academy of Marketing research grant that supported data collection for this project and to Charlotte Doyle for her assistance in collecting interview data. The authors would also like to thank the Editors, Associate Editor, and reviewers for their detailed and constructive feedback throughout the review process.

Abidin Crystal ( 2015 ), “ Communicative Intimacies: Influencers and Perceived Interconnectedness ,” Ada , 8 , 1 – 16 .

Google Scholar

Abidin Crystal ( 2021 ), “ Mapping Internet Celebrity on TikTok: Exploring Attention Economies and Visibility Labours ,” Cultural Science Journal , 12 ( 1 ), 77 – 103 .

Arnould Eric J. , Arvidsson Adam , Eckhardt Giana M. ( 2021 ), “ Consumer Collectives: A History and Reflections on Their Future ,” Journal of the Association for Consumer Research , 6 ( 4 ), 415 – 28 .

Arvidsson Adam , Caliandro Alessandro ( 2016 ), “ Brand Public ,” Journal of Consumer Research , 42 ( 5 ), 727 – 48 .

Ashforth Blake E. ( 2000 ), Role Transitions in Organizational Life: An Identity-Based Perspective , Mahwah, NJ : Lawrence Erlbaum .

Google Preview

Audrezet Alice , de Kerviler Gwarlann , Moulard Julie Guidry ( 2020 ), “ Authenticity under Threat: When Social Media Influencers Need to Go beyond Self-Presentation ,” Journal of Business Research , 117 , 557 – 69 .

Berryman Rachel , Kavka Misha ( 2017 ), “ ‘I Guess a Lot of People See Me as a Big Sister or a Friend’: The Role of Intimacy in the Celebrification of Beauty Vloggers ,” Journal of Gender Studies , 26 ( 3 ), 307 – 20 .

Biddle Bruce J. ( 1979 ), Role Theory: Expectations, Identities, and Behaviors , New York : Academic Press .

Biddle Bruce J. ( 1986 ), “ Recent Developments in Role Theory ,” Annual Review of Sociology , 12 ( 1 ), 67 – 92 .

Biraghi Silvia , Gambetti Rossella , Pace Stefano ( 2018 ), “ Between Tribes and Markets: The Emergence of a Liquid Consumer-Entrepreneurship ,” Journal of Business Research , 92 , 392 – 402 .

Boyaval Marine , Herbert Maud ( 2018 ), “ One for All and All for One? The Bliss and Torment in Communal Entrepreneurship ,” Journal of Business Research , 92 , 412 – 22 .

Breves Priska , Amrehn Jana , Heidenreich Anna , Liebers Nicole , Schramm Holger ( 2021 ), “ Blind Trust? The Importance and Interplay of Parasocial Relationships and Advertising Disclosures in Explaining Influencers’ Persuasive Effects on Their Followers ,” International Journal of Advertising , 40 ( 7 ), 1209 – 29 .

Brinks Verena , Ibert Oliver ( 2015 ), “ Mushrooming Entrepreneurship: The Dynamic Geography of Enthusiast-Driven Innovation ,” Geoforum , 65 , 363 – 73 .

Brooks Gillian , Drenten Jenna , Piskorski Mikolaj J. ( 2021 ), “ Influencer Celebrification: How Social Media Influencers Acquire Celebrity Capital ,” Journal of Advertising , 50 ( 5 ), 528 – 47 .

Campbell Colin , Grimm Pamela E. ( 2019 ), “ The Challenges Native Advertising Poses: Exploring Potential Federal Trade Commission Responses and Identifying Research Needs ,” Journal of Public Policy & Marketing , 38 ( 1 ), 110 – 23 .

Cocker Hayley , Mardon Rebecca , Daunt Kate ( 2021 ), “ Social Media Influencers & Transgressive Celebrity Endorsement in Consumption Community Contexts ,” European Journal of Marketing , 55 ( 7 ), 1841 – 72 .

Cova Bernard , Guercini Simone ( 2016 ), “ Passion and Entrepreneurship: Toward a Tribal Entrepreneur? ,” Revue de L’Entrepreneuriat , 15 ( 2 ), 15 – 42 .

Cova Bernard , Kozinets Robert V. , Shankar Avi ( 2007 ), “Tribes, Inc.: the New World of Tribalism,” in Consumer Tribes , eds. Cova Bernard , Kozinets Robert V. and Shankar Avi , New York : Routledge , 3 – 26 .

Debenedetti Alain , Oppewal Harmen , Arsel Zeynep ( 2014 ), “ Place Attachment in Commercial Settings: A Gift Economy Perspective ,” Journal of Consumer Research , 40 ( 5 ), 904 – 23 .

Dalli Daniele ( 2021 ), “ Consumers and Consumption: From Individual, to Collective, and Beyond ,” Italian Journal of Marketing , 2021 ( 1–2 ), 1 – 4 .

Droesch Blake ( 2020 ), “Influencers More Likely to Inspire Gen Zer and Millenial Purchases,” eMarketer. https://www.emarketer.com/content/influencers-more-likely-to-inspire-gen-zer-and-millennial-purchases . Last accessed January 1, 2023.

Duffy Brooke E. , Miltner Kate M. , Wahlstedt Amanda ( 2022 ), “ Policing “Fake” Femininity: Authenticity, Accountability, and Influencer Antifandom ,” New Media & Society , 24 ( 7 ), 1657 – 76 .

Ebbers Joris J. , Wijnberg Nachoem M. ( 2017 ), “ Betwixt and Between: Role Conflict, Role Ambiguity and Role Definition in Project-Based Dual-Leadership Structures ,” Human Relations , 70 ( 11 ), 1342 – 65 .

Fournier Susan , Lee Lara ( 2009 ), “ Getting Brand Communities Right ,” Harvard Business Research , 87 ( 4 ), 105 – 11 .

Gannon Valerie , Prothero Andrea ( 2018 ), “ Beauty Bloggers and YouTubers as a Community of Practice ,” Journal of Marketing Management , 34 ( 7–8 ), 592 – 619 .

Giesler Markus ( 2008 ), “ Conflict and Compromise: Drama in Marketplace Evolution ,” Journal of Consumer Research , 34 ( 6 ), 739 – 53 .

Goffman Erving ( 1959 ), The Presentation of Self in Everyday Life , NY : Doubleday .

Goode William J. ( 1960 ), “ A Theory of Role Strain ,” American Sociological Review , 25 ( 4 ), 483 – 96 .

Goulding Christina , Saren Michael ( 2007 ), “‘Gothic’ Entrepreneurs: A Study of the Subcultural Commodification Process,” in Consumer Tribes , ed. Bernard Cova, Robert Kozinets, and Avi Shankar, Oxford : Butterworth Heinemann , 227 – 42 .

Granovetter Mark ( 1985 ), “ Economic Action and Social Structure: The Problem of Embeddedness ,” American Journal of Sociology , 91 ( 3 ), 481 – 510 .

Gray Jonathan ( 2005 ), “ Antifandom and the Moral Text: Television without Pity and Textual Dislike ,” American Behavioral Scientist , 48 ( 7 ), 840 – 58 .

Grayson Kent ( 2007 ), “ Friendship versus Business in Marketing Relationships ,” Journal of Marketing , 71 ( 4 ), 121 – 39 .

Greenhaus Jeffrey H. , Powell Gary N. ( 2006 ), “ When Work and Family Are Allies: A Theory of Work-Family Enrichment ,” Academy of Management Review , 31 ( 1 ), 72 – 92 .

Heide Jan B. , Wathne Kenneth H. ( 2006 ), “ Friends, Businesspeople, and Relationship Roles: A Conceptual Framework and a Research Agenda ,” Journal of Marketing , 70 ( 3 ), 90 – 103 .

Helmore Edward ( 2021 ), “Pay Us a Fair Share for All the ‘Likes’ We Earn, Demand Influencers,” The Guardian https://www.theguardian.com/media/2021/sep/26/pay-us-a-fair-share-for-all-the-likes-we-earn-demand-influencers . Last accessed January 1, 2023.

Hou Mingyi ( 2019 ), “ Social Media Celebrity and the Institutionalization of YouTube ,” Convergence: The International Journal of Research into New Media Technologies , 25 ( 3 ), 534 – 53 .

Katz Elihu , Lazarsfeld Paul F. ( 1955 ), Personal Influence , Glencoe, IL : The Free Press .

Khamis Susie , Ang Lawrence , Welling Raymond ( 2017 ), “ Self-Branding, ‘Micro-Celebrity’ and the Rise of Social Media Influencers ,” Celebrity Studies , 8 ( 2 ), 191 – 208 .

Kjeldgaard Dannie ( 2017 ), “Karl Polanyi—Whence the Marketing Mind?,” in Canonical Authors in Consumption Theory , eds. Askegaard Søren , Heilbrunn Benoît , London : Routledge , 34 – 39 .

Kozinets Robert V. ( 2020 ), Netnography: The Essential Guide to Qualitative Social Media Research , 3rd Edition, London : SAGE Publications Ltd .

Kozinets Robert V. , , Daniela Abrantes Ferreira , and , Paula Chimenti ( 2021 ), “ How Do Platforms Empower Consumers? Insights from the Affordances and Constraints of Reclame Aqui ,” Journal of Consumer Research , 48 ( 3 ), 428 – 55 .

Kozinets Robert V. , de Valck Kristine , Wojnicki Andrea C. , Wilner Sarah J. S. ( 2010 ), “ Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities ,” Journal of Marketing , 74 ( 2 ), 71 – 89 .

Leary Mark R. ( 1996 ), Self-Presentation: Impression Management and Interpersonal Behaviour , New York: Routledge

Leigh Thomas W. , Peters Cara , Shelton Jeremy ( 2006 ), “ The Consumer Quest for Authenticity: The Multiplicity of Meanings within the MG Subculture of Consumption ,” Journal of the Academy of Marketing Science , 34 ( 4 ), 481 – 93 .

Mardon Rebecca , Molesworth Mike , Grigore Georgiana ( 2018 ), “ YouTube Beauty Gurus and the Emotional Labour of Tribal Entrepreneurship ,” Journal of Business Research , 92 , 443 – 54 .

Martin Diane M. , Schouten John W. ( 2014 ), “ Consumption-Driven Market Emergence ,” Journal of Consumer Research , 5 ( 40 ), 855 – 70 .

Marwick Alice ( 2015 ), ”You May Know Me from YouTube: (Micro)-Celebrity in Social Media.“ in A Companion to Celebrity , ed. Marshall D. , Redmond S. , Hoboken, NJ : Wiley-Blackwell , 333 – 50 .

Marwick Alice ( 2017 ), “Microcelebrity, Self-Branding, and the Internet,” in The Blackwell Encyclopedia of Sociology , ed. Ritzer George , Oxford, UK: John Wiley & Sons, 1 – 3 .

Marwick Alice , boyd danah ( 2011 ), “ To See and Be Seen: Celebrity Practice on Twitter ,” Convergence: The International Journal of Research into New Media Technologies , 17 ( 2 ), 139 – 58 .

McAlexander James H. , Leavenworth Dufault Beth , Martin Diane M. , Schouten John W. ( 2014 ), “ The Marketization of Religion: Field, Capital, and Consumer Identity ,” Journal of Consumer Research , 41 ( 3 ), 858 – 75 .

McCracken Grant ( 1989 ), “ Who is the Celebrity Endorser? Cultural Foundations of the Endorsement Process ,” Journal of Consumer Research , 16 ( 3 ), 310 – 21 .

McQuarrie Edward F. , Miller Jessica , Phillips Barbara J. ( 2013 ), “ The Megaphone Effect: Taste and Audience in Fashion Blogging ,” Journal of Consumer Research , 40 ( 1 ), 136 – 58 .

Montgomery James D. ( 1998 ), “ Toward a Role-Theoretic Conception of Embeddedness ,” American Journal of Sociology , 104 ( 1 ), 92 – 125 .

Muñiz Albert M. Jr., , O’Guinn Thomas C. ( 2001 ), “ Brand Community ,” Journal of Consumer Research , 27 ( 4 ), 412 – 32 .

Oppenheim Maya ( 2016 ), “YouTube star Zoella Reportedly Earns at Least £50,000 a Month According to Newly Released Accounts,” The Independent , http://www.independent.co.uk/news/people/zoella-youtube-earnings-50000-sunday-times-alfie-deyes-a6928666.html . Last accessed January 1, 2023.

Petter Olivia ( 2019 ), “Zoe Sugg Named Highest-Earning Female Social Media Influencer Under 30,” The Independent . https://www.independent.co.uk/life-style/women/zoe-sugg-net-worth-zoella-influencer-richest-highest-earning-list-a9175441.html . Last accessed January 1, 2023.

Polanyi Karl ( 1944 ), The Great Transformation , New York : Farr & Rinehart .

Price Linda L. , Arnould Eric J. ( 1999 ), “ Commercial Friendships: Service Provider-Client Relationships in Context ,” Journal of Marketing , 63 , 38 – 56 .

Scaraboto Daiane ( 2015 ), “ Selling, Sharing, and Everything in between: The Hybrid Economies of Collaborative Networks ,” Journal of Consumer Research , 42 ( 1 ), 152 – 76 .

Scholz Joachim ( 2021 ), “ How Consumers Consume Social Media Influence ,” Journal of Advertising , 50 ( 5 ), 510 – 27 .

Schouten John W. , McAlexander James H. ( 1995 ), “ Subcultures of Consumption: An Ethnography of the New Bikers ,” Journal of Consumer Research , 22 ( 1 ), 43 – 61 .

Senft Theresa M. ( 2008 ), Camgirls: Celebrity and Community in the Age of Social Networks , New York : Peter Lang .

Senft Theresa M. ( 2013 ), “Microcelebrity and the Branded Self,” in A Companion to New Media Dynamics , eds. Hartley John , Burgess Jean , Bruns Axel , New York : Wiley-Blackwell, 346 – 54 .

Seregina Anastasia , Weijo Henri A. ( 2017 ), “ Play at Any Cost: How Cosplayers Produce and Sustain Their Ludic Communal Consumption Experiences ,” Journal of Consumer Research , 44 (1), 139 – 59 .

Sieber Sam D. ( 1974 ), “ Toward a Theory of Role Accumulation ,” American Sociological Review , 39 ( 4 ), 567 – 78 .

Skaff Marilyn M. , Pearlin Leonard I. ( 1992 ), “ Caregiving: Role Engulfment and the Loss of Self ,” The Gerontologist , 32 ( 5 ), 656 – 64 .

Sluss , David M. , van Dick Rolf , Thompson Bryant ( 2011 ), “Role Theory in Organizations: A Relational Perspective,” in APA Handbook of Industrial and Organisational Psychology , ed. Zedeck Sheldon , Washington : APA , 505 – 34 .

Smith Andrew N. , Fischer Eileen ( 2021 ), “ Pay Attention, Please! Person Brand Building in Organized Attention Economies ,” Journal of the Academy of Marketing Science , 49 ( 2 ), 258 – 79 .

Stryker Sheldon ( 1968 ), “ Identity Salience and Role Performance: The Relevance of Symbolic Interaction Theory for Family Research ,” Journal of Marriage and the Family , 30 ( 4 ), 558 – 64 .

Sweney Mark ( 2021 ), “Cristiano Ronaldo Shoots to Top of Instagram Rich List,” The Guardian. https://www.theguardian.com/technology/2021/jun/30/cristiano-ronaldo-shoots-to-top-of-instagram-rich-list . Last accessed January 1, 2023.

The Economist ( 2022 ), “The Business of Influencing is Not Frivolous. It’s Serious,” The Economist . https://www.economist.com/business/2022/04/02/the-business-of-influencing-is-not-frivolous-its-serious . Last accessed January 1, 2023.

Thomas Tandy Chalmers. , Price Linda L. , Schau Hope Jensen ( 2013 ), “ When Differences Unite: Resource Dependence in Heterogeneous Consumption Communities ,” Journal of Consumer Research , 39 ( 5 ), 1010 – 33 .

Thompson Craig J. ( 1997 ), “ Interpreting Consumers: A Hermeneutical Framework for Deriving Marketing Insights from the Texts of Consumers' Consumption Stories ,” Journal of Marketing Research , 34 ( 4 ), 438 – 55 .

Thompson Craig J. , Coskuner-Balli Gokcen ( 2007 ), “ Countervailing Market Responses to Corporate Co-Optation and the Ideological Recruitment of Consumption Communities ,” Journal of Consumer Research , 34 ( 2 ), 135 – 52 .

Thornton Sarah ( 1995 ), Club Cultures: Music, Media and Subcultural Capital , Cambridge : Polity .

Varman Rohit , Costa Janeen Arnold ( 2008 ), “ Embedded Markets, Communities, and the Invisible Hand of Social Norms ,” Journal of Macromarketing , 28 ( 2 ), 141 – 56 .

Van Sell Mary , Brief Arthur P. , Schuler Randall S. ( 1981 ), “ Role Conflict and Role Ambiguity: Integration of the Literature and Directions for Future Research ,” Human Relations , 34 ( 1 ), 43 – 71 .

Veloutsou Cleoptra , Black Iain ( 2020 ), “ Creating and Managing Participative Brand Communities: The Roles Members Perform ,” Journal of Business Research , 117 , 873 – 85 .

Webber David M. ( 2017 ), “ ‘Playing on the Break’: Karl Polanyi and the Double-Movement ‘against Modern Football’ ,” International Review for the Sociology of Sport , 52 ( 7 ), 875 – 93 .

Weijo Henri , Hietanen Joel , Mattila Pekka ( 2014 ), “ New Insights into Online Consumption Communities and Netnography ,” Journal of Business Research , 67 ( 10 ), 2072 – 8 .

Weinberger Michelle F. , Wallendorf Melanie ( 2012 ), “ Intracommunity Gifting at the Intersection of Contemporary Moral and Market Economies ,” Journal of Consumer Research , 39 ( 1 ), 74 – 92 .

Weinstein Eugene A. , Deutschberger Paul ( 1963 ), “ Some Dimensions of Altercasting ,” Sociometry , 26 ( 4 ), 454 – 66 .

Wenger Etienne , McDermott Richard , Snyder William M. ( 2002 ), A Guide to Managing Knowledge: Cultivating Communities of Practice , Boston : Harvard Business School .

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The Prediction of Consumer Behavior from Social Media Activities

Nada ali hakami.

1 Computer Science Department, College of Computer Science and Information Technology, Jazan University, Jazan 82822-6694, Saudi Arabia

Hanan Ahmed Hosni Mahmoud

2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Associated Data

The data presented in this study are available on request from the corresponding author.

Consumer behavior variants are evolving by utilizing advanced packing models. These models can make consumer behavior detection considerably problematic. New techniques that are superior to customary models to be utilized to efficiently observe consumer behaviors. Machine learning models are no longer efficient in identifying complex consumer behavior variants. Deep learning models can be a capable solution for detecting all consumer behavior variants. In this paper, we are proposing a new deep learning model to classify consumer behavior variants using an ensemble architecture. The new model incorporates two pretrained learning algorithms in an optimized fashion. This model has four main phases, namely, data gathering, deep neural modeling, model training, and deep learning model evaluation. The ensemble model is tested on Facemg BIG-D15 and TwitD databases. The experiment results depict that the ensemble model can efficiently classify consumer behavior with high precision that outperforms recent models in the literature. The ensemble model achieved 98.78% accuracy on the Facemg database, which is higher than most machine learning consumer behavior detection models by more than 8%.

1. Introduction

In recent years, it is feasible to perform everyday activities using the Internet including social media interaction. All of these activities include consumers submitting reviews online [ 1 , 2 , 3 ]. Consumers often use the Internet to launch consumer-related communication. Consumer behavior is any message or social media-based communication that performs reviewing language. Consumer behavior can be classified into various types. Consumer behavior variants can include consumer reviews. New consumer behavior variants utilize various models such as encryption and packing to remain visible to consumer reviews system [ 3 ].

We have to detect consumer behavior as soon as it spreads into the social media platform. Consumer behavior prediction is the procedure of investigating review messaging in social media interactions and predicting if it is consumer/non-consumer behavior. Consumer behavior can be classified as immediate or future. Identifying consumer behavior requires three steps:

• (1) Consumer behavior messages and social media interaction are analyzed with proper tools.
• (2) Dynamic features such as timing are extracted the interaction data.
• (3) Parameters are assembled in specified sets and are used to differentiate consumer from non-consumer behavior.

To enhance the detection rate, different techniques such as data science, cloud computing, deep learning, and computerized learning models are utilized. Various consumer behavior prediction techniques utilize these technologies. These models are signature checking, behavioral analysis and stochastic learning models [ 2 , 4 ]. Signature-checking models are effective for identifying similar variants of consumer behavior. However, they fail to detect formerly unnoticed consumer behavior. Although stochastic models can detect unknown consumer behavior, they cannot detect more complex consumer behavior clarifications.

A deep learning model can be utilized as a standard to eradicate the shortcomings of the current consumer behavior classification models. Deep learning is utilized extensively in different paradigms such as representation processing, human emotion recognition [ 5 ], and action recognition [ 6 , 7 , 8 ]. Nevertheless, it has not been utilized adequately in the retail research, particularly consumer behavior detection. Deep learning is an artificial intelligence model operating on an artificial neural mechanism. Deep learning employs supervision. To enhance the precision, various models have been utilized such as deep belief techniques. Deep learning models have many advantages over customary models: for instance, deep learning models can mine significant data from the input to lessen the training requirements. Deep learning can also use representations resourcefully as well processing big databases while reducing time and enhancing precision.

Our research presents a new ensemble learning model for consumer Internet behavior classification. In the ensemble model, consumer behavior data are collected from BIG-D15, Facemg, and TwitD databases. Consumer behavior representations are transformed into grayscale and then used as input to the training module. After the data procurement section is complete, the ensemble model extracts high-level consumer behavior features from the consumer behavior representations by utilizing the convolution function of our ensemble model. The model then undergoes supervised training. Several deep models are united to build the ensemble learning model using a number of hidden layer activation functions. The experiments performed prove that the ensemble model efficiently mines unique properties for consumer behavior groups. The performance also presented that the ensemble model predicts various consumer behavior classes with the utmost precision with better performance than recent models.

The main contributions of our research are as follows:

• A new ensemble neural model for consumer Internet behavior classification is defined.
• The ensemble model utilizes a new merging technique that has two transfer learning CNNs.
• Unique parameters are mined from the consumer behavior data for the specified classes.
• The ensemble model lessens the parameter dimensionality considerably.
• The ensemble model evaluates consumer behavior databases.
• The performance rates outperform similar models.

The rest of the article is structured as follows: In Section 2 , we clarify the consumer behavior investigation, parameter selection, and classification and survey the current consumer behavior prediction models. The ensemble model is depicted in Section 3 . Section 4 presents and discusses the experiment results. Section 5 lists the limitations of the model and future work directions. Conclusions are given in Section 5 .

2. Related Work

Consumer behavior detection is a long procedure with many phases. Many techniques are utilized in this procedure. The detection of consumer behavior requires analyzing the existing methods. Results are recorded, and parameters are then computed. In the next stage, intelligent methods are utilized to select only the significant parameters [ 9 , 10 , 11 ]. Selected features are used in the training phase of the neural model to distinguish consumer behaviors. Consumer behavior analysis and classification processes are depicted and summarized. To comprehend the reason for a consumer’s behavior, more prediction is employed by identifying the groups of consumer behaviors [ 10 ]. In order to comprehend the consumer behavior mining methods that are applied, this summary is structured as follows: consumer behavior detection platforms, consumer behavior analysis, consumer behavior feature extraction, and classification.

2.1. Over the Internet Consumer Behavior Detection Platforms

Consumer behavior detection models can be performed on computer cloud platforms, mobile, and IoT devices. Smart platforms developed widespread paradigms. Then, these platforms started to become available. In current studies, people widely utilize mobile applications, and IoT platforms are expanding rapidly. Therefore, consumers’ interests have moved to Internet computing paradigms [ 11 , 12 , 13 ]. Cloud computing also aids consumer behavior by simple access to different databases [ 13 ]. Recent papers on consumer behavior classification models are concerned with IoT and Internet communication. Most published research paved the way to deep learning techniques.

2.2. Consumer Behavior Analysis Model

Consumer behavior samples must be investigated to discover consumer behaviors [ 14 ]. Consumer behavior investigation is a significant procedure for online retail. Consumer behavior detection is performed to answer questions such as the nature of the consumer behavior structure, the plan and place where the consumer activity will take place, and the spread network for identifying the review scores. Consumer behaviors are split into two classes, local and global. Consumer behavior undergoes static and dynamic analysis [ 15 ].

In static analysis, file texts and representation data shared over the Internet are examined by analyzing data without details [ 14 ]. To mine these data, numerous tools can be utilized such as PEiD and MD5deep [ 15 ]. Static analysis is the initial stage of consumer behavior analysis; to achieve better understanding, advanced static investigation is recommended. In advanced analysis, the representations and social media interactions are inspected in detail [ 16 , 17 ]. For this reason, complicated representation splitters are extensively used. In the analysis phase, social media interactions and shared representations are inspected in depth to find out features of consumer behavior. Certain consumer behavior maps can be drawn as a result of reversing social interaction networking. However, due to the huge amount of data on the Internet, performing such analysis needs more time [ 14 , 18 ].

In dynamic analysis, reverse interaction maps are executed, and the behaviors of the involved consumers behavior. The consumer behavior is monitored with process explorer [ 17 ]. In advanced analysis, tools such as WinOlly are utilized. Such tools allow consumer behavior and Internet interaction to process for both reading and sharing [ 14 ].

2.3. Consumer Behavior Parameter Setting

When individual consumer actions are studied, the interaction timeline is recorded. Records will be analyzed to mine consumer behavior parameters. Learning models use previous knowledge from big data. At this phase, assured patterns in the data and unknown values are extracted. In recent research, data mining methods such as n-gram, bags and the net model are utilized when identifying consumer behavior parameters [ 9 , 10 ]. There are parameter selection systems that can be presented for consumer behavior prediction [ 9 , 10 , 11 , 12 ].

2.3.1. Feature Extraction Methods

N-gram models is utilized in many classification models including consumer behavior classification through sentence analysis. N-gram models can use feature mining to extract learning parameters. Once parameters are selected from the consumer behaviors, it can utilize temporal text analysis [ 13 ]. For example, if a sample sentence is S = {1,2,3,4}, 2-gram and 3-gram will be {⟨1,2⟩, ⟨2,3⟩} and {⟨1,2,3⟩, ⟨2,3,4⟩}, respectively. Bag feature extraction models are comparable with n-gram models with high occurrences and are of higher significance than the word location [ 14 ]. Although this technique is an operative grouping method, the high growth in the count of the parameters decreases its efficiency. Other versions of this model group consumer behavior features intelligently [ 14 , 15 , 16 ].

2.3.2. Graph Maps for Feature Extraction

Internet interactions gathered while extracting features include strings, friend networks, messages, and interactions and are represented by diagrams [ 17 , 18 , 19 ], where the map representation is M (N, E) and N is a vertex representing people in a subset of the social media interactions and E are edges representing interaction among the nodes. The problem is that the graph size can grow exponentially over time, and therefore, we use map subsets to approximate the whole map representation s [ 19 ]. However, extracting the map subsets is a hard problem in research, and therefore stochastic or greedy algorithms are employed.

2.3.3. Visual Techniques

Visual parameter mining techniques have several algorithms to mine the parameters of consumer behavior. In one algorithm, consumer behavior text binaries are used as a representation. In this algorithm, a consumer behavior binary is represented and saved as a one-byte vector [ 19 , 20 , 21 ]. In another algorithm, consumer behavior analysis is performed using tools such as IDA Pro [ 22 ]. Then, interaction maps are collected and visualized as representations. In the representation process, models such as graph maps are utilized. In recent research, facts are established that consumer behavior groups and have comparable visual parameters [ 23 , 24 , 25 , 26 ].

2.4. Consumer Behavior Deep Learning Classification Models

Consumer behavior features are extracted by utilizing deep learning or experimental models to predict the consumer behavior. We divide review score classification models to different models comprising signature, machine and deep learning models [ 27 ].

2.4.1. Signature Classification Model

A bit series that represents the interaction map consists of exclusive bits for structures and are utilized in consumer behavior classification [ 28 ]. In the computation, the fixed parameters are mined from the interaction data. The computational process produces outputs by utilizing interaction data and stores them in a database. When the reviewing consumer behavior is marked as actual, the signature is computed and compared with the predetermined signatures as actual. This model is efficient in detecting consumer behavior, but it cannot identify unknown consumer behavior. Additionally, from the results in [ 29 ], consumer behavior classification is not valuable as it is not identifying different consumer behavior types, it is not robust, and it depends on social media interface structure.

The bit-series computation model is introduced by the authors in [ 30 ]. The ensemble model extracts the signatures utilizing a range of identification-based stochastics. Computed signatures are mostly seen in consumer behavior interaction structures. Therefore, false detection rates are reduced. The authors in [ 31 ] presented a basic regular expression-induced signature utilizing caterpillars. The presented model induced computed values and entailed several steps: recognizing the highest projecting series utilizing orientation methods, removing noisy portions, and creating regular expression.

2.4.2. Behavior-Based Consumer Behavior Classification Models

Behavior-based classification models monitor the behaviors in the interaction network. Based on the monitored behaviors, the reviewing consumer is determined to be actual. This model has three portions: mining behaviors, creating features, and classification through machine learning models [ 32 ]. When extracting behaviors, interaction data, shared messages, and posts are utilized. Behaviors are extracted by computing the order of the message sharing and their frequency. Activities are categorized, series are computed, and features can be attained. Granting the interaction structure fluctuates over time, and its overall behavior will not have changed entirely. Hence, numerous consumer behavior types are identified by employing the suggested model. Additionally, consumer behavior has remained formerly unknown, an is predicted.

Consumer behavior classification employing the graph method is clarified by the authors in [ 33 ]. Consumers are converted into a graph such that each vertex denotes a consumer, and the edge denotes a transition among the posts. Using the result of a consumer wording as an input, the links among social media posts are computed. The graph was mined and associated with the existing graph models. The graphs were defined as consumer or non-consumer behavior. Additionally, new actions that are detected during the social media analysis, were dynamically defined in the graph. The authors in [ 34 ] presented a centric model in which consumer behavior interactions are unlike from non-consumer behaviors. By employing the interaction variances, behavior structures were generated from the graph nodes. Further, by employing these sequences, consumer and non-consumer behavior classes were produced.

2.4.3. Stochastic-Based Consumer Behavior Prediction Model

Stochastic classification is a composite classification model that utilizes diverse models together. This classification model uses prior knowledge of rules and machine learning models [ 24 ]. Stochastic models employ both sentiment and behavior features to produce rules. Founded on the generated rules, consumer signatures are formed and utilized to regulate diverse consumer behavior as well as formerly unseen consumer behavior. The model learns by employing definite parameters for validation and irregularities. Even though the hit percentage in identifying unknown consumer behavior is in elevation.

The authors in [ 25 ] presented a deep learning consumer behavior classification model. The model can detect formerly unseen consumer behavior variants. The authors in [ 26 ] clarified a stochastic signature method. In this model, parameters are produced of particles that can be consumer posts, comments, shares, and reviews.

2.4.4. Model Checking Consumer Behavior Prediction Model

In model checking classification, malicious parameters are extracted by employing nonlinear temporal models to detect the parameter dependencies, which are defined as disclaimers [ 32 ]. Consumer behavior parameters are mined by employing the association rules of different actions that utilize unseen behavior. To mark the input as reviewing consumer behavior, the features that are extracted are compared with the former provisions. This presented model is resilient to secrecy and can identify new portion of the consumer behavior variants.

The authors in [ 34 ] presented a confirmation model to identify malicious consumer behavior. In the ensemble model, malicious activities were defined by employing a predicate logic specification model from the assembled social media posts. If the method controller properly identified the specification, the investigated case is defined as consumer or non-consumer behavior. In this model, consumer behaviors are identified among friends of the same consumer types. Additionally, new, unknown but comparable consumer behavior variants are identified. Agreeing to the method, a checking algorithm detected consumer behavior semantic features more precisely than usual classification models, and this enhanced the precision of the classification. A positive consumer behavior classification model that employed a checking algorithm was introduced by the authors in [ 31 ]. The ensemble model can classify various consumers. The model mined features from posts and comments and routinely authenticated them employing the former defined specifications. They employed a novel specification model, NSM. The research depicted good results; the model could classify several types of consumers.

A technique is proposed in [ 30 ] to identify consumer behavior. This model declined the checking difficulty to regulate a Büki pushdown model with figurative factors. Consumer behaviors are transferred into pushdown stacks, and then, by employing the stack predicate logic, the consumer behavior activities are detected. At the final stage, the consumer behavior is mined by comparing it with the pushdown specifications. The presented model is resilient to stealth faulty identification and detects consumer behavior with high precision.

2.4.5. Deep Learning Consumer Behavior Detection Model

Deep learning models are extensions of machine learning that are trained from samples and take over from convolutional neural networks. Deep learning models are employed in representation processing, autonomous vehicles, speech recognition, and also consumer behavior classification. The deep learning classification method performs well with high accuracy and decreases the parameter dimension, but it is not resilient to hacking [ 12 ]. Moreover, forming hidden layers requires more time; constructing more hidden layers will enhance the accuracy slightly while consuming more time. Deep learning models are not yet widely employed in consumer behavior classification, and thus, more research is required to precisely validate this model. The deep learning consumer behavior classification methodologies that are found in the literature are described below.

A deep learning consumer behavior classification model employing two-dimensional consumer behavior features is presented in [ 30 ]. The presented model has three central phases: In the first phase, five matching parameters are mined from reviewing consumers and non-consumer cases; in the next phase, three deep and dropout layers are constructed; in the last phase, the outputs are computed by employing the calibrator metric. At this phase, the approximation of whether the social post is consumer behavior or not is classified. From their results, the model performs well, with 96.7% accuracy with high sensitivity and F1-score.

The authors in [ 31 ] explicated a deep learning network they defined as a multitasking training model for consumer behavior identification. In the presented model, reviewing samples are trained using dynamic learning. Multitasking training permits the model to pre-train even at shallow layers, and the network utilized optimization techniques to reduce the counts of epochs and decrease errors; the researcher claimed that the model accomplished good accuracy in comparison with other deep models. However, the accuracy of the network cannot have increased by having additional deep layers. The authors in [ 32 ] introduced a mixed deep learning model to identify zero-period consumer behavior. The presented model employed multiple hidden layers using a Boltzmann network and short-term memory. It has two stages: model learning and parameter tuning. In the learning phase, training is performed by extracting features using a supervised fashion. At this phase, the features of each post are extracted. Then, in the parameter tuning stage, labelling is performed to split consumer from non-consumer behavior.

Deep learning classification models are operative when identifying consumer behaviors that can be misled by normal posting with harsh words, which yields miss-identification. For example, argumentative posts can deliver misleading inputs to and generate wrong consumer classification. Additionally, true consumers can be unidentified by shifting some characters in the wording. In our ensemble consumer behavior method, essential constraints are employed to diminish the impacts of such reviews.

3. Material and Methods

In this paper, we are presenting our ensemble consumer behavior learning model. This platform is an ensemble deep-CNN model. The ensemble deep-CNN platform as depicted has several stages ( Figure 1 ). In the initial stage, the consumer behavior data are gathered by exhaustive database mining. In the second stage, unimportant and important consumer behavior parameters are extracted employing transfer learning CNN. The final stage starts a supervised learning module.

The ensemble consumer behavior classification model.

The following subsections describe consumer behavior representation and the deep learning model. In the consumer behavior representation, we will present the ensemble consumer behavior variants in binary. In the model description section, the ensemble consumer behavior classification model is explained.

3.1. Consumer Behavior Representation as a Binary Map

Various methods are introduced to transform binary file into maps [ 29 , 30 , 31 ]. This research utilizes the representation of the consumer behavior binary maps [ 27 ]. The required goal is to represent binary maps as a binary representation. Based on our algorithm, the consumer behavior binary file is represented as 8-bits vectors of unsigned integers. Then the binary number B is transformed into its number value employing Equation (1). At the end, the value is combined into a 2D array M and construed as a binary representation. The dimension of matrix M depends upon the consumer behavior file size

3.2. The Ensemble Model for Consumer Behavior Prediction

The ensemble method defines a platform for consumer behavior prediction. This platform is an deep CNN structure. Our model of the ensemble platform is previously depicted in Figure 1 , with consecutive stages: assembly of consumer behavior data, deep CNN structure, training, and testing phases. A flow diagram is depicted where the pre-trained CNN represents a feature extractor module. The first five layers exhibit FC layers for the training module and the Softmax layer.

Primarily, consumer behavior data are composed from several databases such as Facemg [ 27 ], BIG-D15 [ 28 ], and TwitD [ 29 ]. The consumer behavior databases are detailed in the following subsection. Then, the ensemble deep CNN model is depicted. There is a pre-processing phase: the procedure of predicting a proper deep learning architecture to embed in it the consumer behavior classifier. It is revealed in pre-processing that the ensemble technique can deliver better precision. An ensemble module that encompasses both DenseNet201 and Vgg19 CNNs is constructed employing transfer learning networks.

The DenseNet201 [ 32 ] network is a CNN with 50 layers with 5 convolutional layers. The DenseNet201 CNN has Maxpooling functions, classifier and FC neural layers. The DenseNet201 network has 25 million hyper parameters as described in Table 1 . Vgg19 [ 33 ] is a prominent CNN network for large-scale representation recognition. Vgg19 has an architecture of several deep layers; the primary layers are convolutional neural layers. Vgg19 has two normalization operations, two pooling layers, and a final classifier, as depicted in Table 1 . The description of DenseNet201 is depicted in Table 2 .

The description of Vgg19 network [ 33 ].

The description of DenseNet201.

Transfer learning has been examined for facing the various challenges faced in the classification model such as computational time cost and large data dimension. Transfer learning perform feature extraction process employing pre-trained CNN. Then, the classification procedure is performed with support vector machine (SVM) or with a Softmax classifier. This process is accustomed for the ensemble Deep CNN to aid with the above challenges.

The ensemble model associated CNNs with an identical weight to generate a representation map. The learning is then accomplished to attain high precision. The steps are as follows: The transfer learning procedure is completed with the DenseNet201 and Vgg19 networks using three databases in the training phase [ 31 ]. In the second phase, the parameters extracted by the DenseNet201 and Vgg19 networks are joined to produce the feature vector. This produced vector has 2048 dimensions. The features produced by the pre-trained DenseNet201 and Vgg19 are extracted from the final FC layer and depicted in T 6 and 7. Then, the joined feature vector is delivered to the Softmax and the FC layers to achieve normalization. Afterwards, the Softmax classifier produces seven outputs that consist of categories of consumer behaviors, and the FC layers encompass 2048 nodes. The last layer targets the enhancement of the learning ability of the ensemble deep-CNN. Finally, the ensemble deep learning model is tested by employing comprehensive databases in the training module. The description of the ensemble model is depicted in Table 3 .

The description of the ensemble model.

4.1. The Implementation Process

This section describes the implementation process, the experiments, and the evaluation of the ensemble deep-CNN model. The experiments are performed on Intel Core i19 running at 9.6 GHz with 64 GB RAM. Python language was used to implement the model. Data are partitioned into training and validation databases randomly: 70% of the data for training, 15% for validation stage and 15% for the testing stage. The training process of the deep-CNN model was completed in 29 h for 80 epochs on average. Metrics such as accuracy, sensitivity, specificity, and F-score are used. These metrics are calculated as follows:

where TP denotes the count of true positives, FP denotes the count of false positives, TN denotes the count of true negatives, and FN denotes the count of false negatives. The metrics above are used to infer the performance of the ensemble model. The ensemble model is compared with two deep neural models. Figure 2 , Figure 3 and Figure 4 depict the metric evaluation of the introduced model using an ensemble of Vgg19 and DenseNet201 deep models for each database. Table 1 depicts the initial parameters values of the selected configuration for the ensemble deep architecture used for the Facemg, BIG-D15, and TwitD databases as depicted in Table 4 .

The performance of Vgg19, DenseNet201, and the ensemble models for the Facemg database.

The performance of Vgg19, DenseNet201, and the ensemble models for the BIG-D15 database.

The performance of Vgg19, DenseNet201, and the ensemble models for the TwitD database.

Parameters of the Facemg, BIG-D15, and TwitD databases.

4.2. Benchmark Database

Experiments are performed on three benchmark databases. These are Facemg, BIG-D15, and TwitD. These databases are described below.

The Facemg database [ 27 ] has 9000 consumer behavior instances. Each single consumer behavior instance in the database fits into one of 20 consumer behavior classes. The count of instances fitting into a consumer behavior class varies across the database. The consumer behavior classes include different types of weapons (13 classes), bombs (5 classes including asking how to make a bomb or purchase materials related to bombs), suicide reviews and killing wordings (2 words).

The BIG-D15 database [ 28 ] contains 22,000 consumer behavior instances belonging to 9 classes include different types of weapons (7 classes), bombs (one class), and suicide reviews (one class). Similar to the Facemg database, the count of consumer behavior instances over defined groups is not equally spread. A single consumer behavior instance is mapped to one eight-bit map representing a hexadecimal number representing the class number. We used the bytes to form a consumer behavior representation in our simulation.

The TwitD database [ 29 ] has 9000 consumer behavior instances for training and 5000 consumer behavior instances for testing fitting into to 20 consumer behavior classes. Each class has 450 instances for training and variable instances for testing. The consumer behavior classes are the same as the classes of the first database.

4.3. Evaluation

Assessment metrics depict the performance of the ensemble consumer behavior classification technique. The direct outcome of the classification model is a score to comprehend the accuracy of a model [ 18 ]. Accordingly, different performance scores stated in the results are employed to depict the efficiency of the presented models. The performance scores are accuracy and the Dice metric. Figure 2 , Figure 3 and Figure 4 depict the performance of the Vgg19 and DenseNet201 deep models and ensemble models for the Facemg, BIG-D15, and TwitD databases. In agreement with these charts, it can be specified that the ensemble model performs other deep learning architectures. Our model performance also depicts comparable results for the three databases, while the results for the compared two deep learning models fluctuate considerably for the three databases. The aforementioned settings indicate that our model is more reliable and has higher accuracy in comparison with the other models.

Additionally, consumer behavior variants are examined using the metrics. Table 5 , Table 6 and Table 7 depict the confusion matrices for the BIG-D15 database for nine consumer behavior classes of Vgg19, DenseNet201, and the ensemble models.

Statistics metrics for the compared models for the Facemg dataset.

Statistics metrics for the compared models for the BIG-D15 dataset.

Statistics metrics for the compared models for the TwitD dataset.

Performance rates for each consumer behavior class are verified with the statistics. It is detected that our ensemble model gives higher results for all consumer behavior classifications excluding casual wording class. The DenseNet201 model delivers a higher classification of the consumer behavior variant compared with other models.

We also compared the ensemble model with the state-of-the-art models. Table 5 , Table 6 and Table 7 depict the accuracy results for the Facemg, BIG-D15, and TwitD databases for the ensemble model and other models, respectively. It should be clarified that the ensemble model performs better than the state-of- the-art models.

4.4. Discussion

In this research, we presented a novel deep learning model to predict different consumer behavior trends using an ensemble architecture This research was focused on the integration of two pre-trained optimized learning algorithms. This model maintains four phases of data gathering, deep modeling, training, and model evaluation. The ensemble model is tested on three social media databases. The experimental results proved the efficiently of consumer behavior trends classification with high precision that outperforms recent models in the literature.

Implications

This study contributes to the literature on consumer behavior trends classification in a number of ways. Firstly, we present an ensemble deep learning model of the consumer behavior trends such as recurrent purchases and loyalty in replying to the social media marketing content. The main contributions of our model are: defining unique parameters through data mining techniques from the consumer behavior data for the specified classes. Additionally, the parameter dimensionality is reduced considerably for faster learning and classification time.

This research also contributes to enterprises who reflect using social media as a marketing channel. The experimental results recommend to digital marketing personal the significance of using social media to influence consumer behavioral trends. Based on the first set of experiments, testing depicted that our scoring model of consumer behavior variants has an important impact on defining consumer satisfaction through social media content sharing. For future research based on our findings, we can encourage and design an interaction marketing model and apply deep learning model on consumer interaction in a more efficient way.

The second set of experiments, which computed the true positive and true negative rates as well as testing kappa coefficient, proved the precision of our model compared with the ground truth and that it attains higher sensitivity and specificity than other deep learning models.

Designing digital marketing strategies based on our scoring technique (from the extracted parameters), based on the deep learning and mining approaches, the quality of enterprises towards consumers can certainly be developed.

5. Conclusions

Consumer behavior classification models effectively identify consumer behavior variants that represent serious consumer behavior in the social media domain that can represent real-life consumers. Unknown people behind the screen with different languages and wordings make the consumer behavior classification a difficult process. Our research ensemble a new merged learning model that efficiently identify consumer behavior classes. The ensemble model employs comprehensive pre-trained models that depend on the transfer learning model. The data on consumer behavior groups were collected by employing several exhaustive databases. Then, the features are mined, and the parameters are computed by employing transfer learning models. Additionally, the ensemble model achieves deep parameter extraction.

The central role of our proposed hybrid model is to unite two optimized deep learning models. The ensemble model is tested and validated on Facemg, BIG-D15, and TwitD databases. The suggested ensemble model is compared versus the joined models individually. The experiment results established that the ensemble model can efficiently predict consumer behavior with high accuracy and Dice score. It is also found that our model is effective and decreases the feature representation space. The ensemble model was compared against other deep learning models. The experiments attained revealed the improvement and reliability of our model over other models. On the other hand, a few consumer behavior instances were not predicted properly. This is because those consumer behavior variants employed unseen mystification wording and depicted the same features with several consumer behavior variants.

Acknowledgments

We would like to thank Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R113), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding Statement

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting project number (PNURSP2022R113), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author Contributions

Conceptualization, H.A.H.M. and N.A.H.; methodology, H.A.H.M.; software, H.A.H.M.; validation, H.A.H.M. and N.A.H.; formal analysis, H.A.H.M.; investigation, H.A.H.M.; resources, H.A.H.M.; data curation, H.A.H.M.; writing—original draft preparation, H.A.H.M.; writing—review and editing, H.A.H.M.; visualization, H.A.H.M.; supervision, H.A.H.M.; project administration, H.A.H.M.; funding acquisition, All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicabale.

Data Availability Statement

Conflicts of interest.

The authors declare that they have no conflicts of interest to report regarding the present study.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Americans’ Social Media Use

Youtube and facebook are by far the most used online platforms among u.s. adults; tiktok’s user base has grown since 2021, table of contents.

• Which social media sites do Americans use most?
• TikTok sees growth since 2021
• Stark age differences in who uses each app or site
• Other demographic differences in use of online platforms
• Acknowledgments
• 2023 National Public Opinion Reference Survey (NPORS) Methodology

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A .

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

Social media platforms faced a range of controversies in recent years, including concerns over misinformation and data privacy . Even so, U.S. adults use a wide range of sites and apps, especially YouTube and Facebook. And TikTok – which some Congress members previously called to ban – saw growth in its user base.

These findings come from a Pew Research Center survey of 5,733 U.S. adults conducted May 19-Sept. 5, 2023.

YouTube by and large is the most widely used online platform measured in our survey. Roughly eight-in-ten U.S. adults (83%) report ever using the video-based platform.

While a somewhat lower share reports using it, Facebook is also a dominant player in the online landscape. Most Americans (68%) report using the social media platform.

Additionally, roughly half of U.S. adults (47%) say they use Instagram .

The other sites and apps asked about are not as widely used , but a fair portion of Americans still use them:

• 27% to 35% of U.S. adults use Pinterest, TikTok, LinkedIn, WhatsApp and Snapchat.
• About one-in-five say they use Twitter (recently renamed “X”) and Reddit.  

This year is the first time we asked about BeReal, a photo-based platform launched in 2020. Just 3% of U.S. adults report using it.

Recent Center findings show that YouTube also dominates the social media landscape among U.S. teens .

One platform – TikTok – stands out for growth of its user base. A third of U.S. adults (33%) say they use the video-based platform, up 12 percentage points from 2021 (21%).

The other sites asked about had more modest or no growth over the past couple of years. For instance, while YouTube and Facebook dominate the social media landscape, the shares of adults who use these platforms has remained stable since 2021.

The Center has been tracking use of online platforms for many years. Recently, we shifted from gathering responses via telephone to the web and mail. Mode changes can affect study results in a number of ways, therefore we have to take a cautious approach when examining how things have – or have not – changed since our last study on these topics in 2021. For more details on this shift, please read our Q&A .

Adults under 30 are far more likely than their older counterparts to use many of the online platforms. These findings are consistent with previous Center data .

Age gaps are especially large for Instagram, Snapchat and TikTok – platforms that are used by majorities of adults under 30. For example:

• 78% of 18- to 29-year-olds say they use Instagram, far higher than the share among those 65 and older (15%).
• 65% of U.S. adults under 30 report using Snapchat, compared with just 4% of the oldest age cohort.
• 62% of 18- to 29-year-olds say they use TikTok, much higher than the share among adults ages 65 years and older (10%).
• Americans ages 30 to 49 and 50 to 64 fall somewhere in between for all three platforms.

YouTube and Facebook are the only two platforms that majorities of all age groups use. That said, there is still a large age gap between the youngest and oldest adults when it comes to use of YouTube. The age gap for Facebook, though, is much smaller.

Americans ages 30 to 49 stand out for using three of the platforms – LinkedIn, WhatsApp and Facebook – at higher rates. For instance, 40% of this age group uses LinkedIn, higher than the roughly three-in-ten among those ages 18 to 29 and 50 to 64. And just 12% of those 65 and older say the same. 

Overall, a large majority of the youngest adults use multiple sites and apps. About three-quarters of adults under 30 (74%) use at least five of the platforms asked about. This is far higher than the shares of those ages 30 to 49 (53%), 50 to 64 (30%), and ages 65 and older (8%) who say the same.  

Refer to our social media fact sheet for more detailed data by age for each site and app.

A number of demographic differences emerge in who uses each platform. Some of these include the following:

• Race and ethnicity: Roughly six-in-ten Hispanic (58%) and Asian (57%) adults report using Instagram, somewhat higher than the shares among Black (46%) and White (43%) adults. 1
• Gender: Women are more likely than their male counterparts to say they use the platform.
• Education: Those with some college education and those with a college degree report using it at somewhat higher rates than those who have a high school degree or less education.
• Race and ethnicity: Hispanic adults are particularly likely to use TikTok, with 49% saying they use it, higher than Black adults (39%). Even smaller shares of Asian (29%) and White (28%) adults say the same.
• Gender: Women use the platform at higher rates than men (40% vs. 25%).
• Education: Americans with higher levels of formal education are especially likely to use LinkedIn. For instance, 53% of Americans with at least a bachelor’s degree report using the platform, far higher than among those who have some college education (28%) and those who have a high school degree or less education (10%). This is the largest educational difference measured across any of the platforms asked about.

Twitter (renamed “X”)

• Household income: Adults with higher household incomes use Twitter at somewhat higher rates. For instance, 29% of U.S. adults who have an annual household income of at least $100,000 say they use the platform. This compares with one-in-five among those with annual household incomes of $70,000 to $99,999, and around one-in-five among those with annual incomes of less than $30,000 and those between $30,000 and $69,999.
• Gender: Women are far more likely to use Pinterest than men (50% vs. 19%).
• Race and ethnicity: 54% of Hispanic adults and 51% of Asian adults report using WhatsApp. This compares with 31% of Black adults and even smaller shares of those who are White (20%).

• Estimates for Asian adults are representative of English speakers only. ↩

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Can a debt collector contact me through social media?

A debt collector can contact you on social media, but they must follow certain rules and tell you how you can opt out of social media communications.

The message must be private.

A debt collector can only communicate with you on social media platforms about a debt if the message is private. A debt collector cannot contact you on social media about a debt if the message is viewable by the general public or viewable by your friends, contacts, or followers on the platform. This would include your publicly visible profile page or any part of the platform where other people can see the message.

The debt collector must identify themself.

If a debt collector attempts to send you a private message requesting to add you as a friend or contact, the debt collector must identify themself as a debt collector.

They must include a way for you to stop receiving messages from them.

Even when a debt collector properly identifies themself in a private social media message, they must give you a simple way to opt out of receiving further communications from them on that social media platform.

Learn more about what to do if a debt collector contacts you , and the rules they must follow.

If you're having an issue with debt collection, you can submit a complaint with the CFPB online or by calling (855) 411-CFPB (2372) .

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• DOI: 10.1109/InC460750.2024.10649139
• Corpus ID: 272372416

Detecting Infectious Disease Based on Social Media Data Using BERT Model

• Rajarshi Barman , Kavitha Rajamohan
• Published in IEEE International Conference… 15 March 2024
• Medicine, Computer Science, Environmental Science
• 2024 IEEE International Conference on Contemporary Computing and Communications (InC4)

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22 References

Bert-deep cnn: state of the art for sentiment analysis of covid-19 tweets, performance of multiple pretrained bert models to automate and accelerate data annotation for large datasets., survey of bert-base models for scientific text classification: covid-19 case study, cobert: covid-19 question answering system using bert, sentiment analysis on the impact of coronavirus in social life using the bert model, covid-19 sensing: negative sentiment analysis on social media in china via bert model, seasonal and regional changes in temperature projections over the arabian peninsula based on the cmip5 multi-model ensemble dataset, regional influenza prediction with sampling twitter data and pde model, sentiment analysis: from binary to multi-class classification: a pattern-based approach for multi-class sentiment analysis in twitter, detecting changes in rainfall pattern and seasonality index vis-à-vis increasing water scarcity in maharashtra, related papers.

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Ireland’s youngest female councillor graduates from University of Limerick

Newly elected Clare County Councillor, Rachel Hartigan credits her success to working twice as hard as the average candidate, as she graduated from University of Limerick today (Thursday) with a Bachelor of Arts in European Studies . 

Aged 22, Cllr Hartigan might be the youngest female councillor in the country, but she is no stranger to politics, having studied it in UL, been an active member of Ógra Fianna Fáil, and interned for Clare TD Cathal Crowe. 

It was during her summer internship with Deputy Crowe that Rachel first considered running in the local elections.  

“I never would have seen myself running for elected politics”, she explained, “but working in Cathal's office, a lot of the queries coming in were what I would imagine a local councillor should really be dealing with. 

“And that's when I realised I didn't know who my local councillor was, which seemed bizarre because I'm a politics student, interning in my TD's office, so I'm politically engaged.  

“And there's a lot that a local councillor deals with that has a huge impact on people's day-to-day lives, and I felt like we were really missing that strong voice.” 

Cllr Hartigan also credits a lack of representation amongst local councillors as a key factor that “spurred” her to action: “I think the median age of a councillor in Ireland is somewhere in the 70s bracket and I felt like that was extremely unfair. 

“When we look at why younger people don't come out in droves to vote a lot of the time, what stood out to me was we can't identify with our politicians. 

“We don't feel like they speak for us and they don't take the time to get to know what our issues are and what's important to us. 

“We're kind of written off and cast aside a lot of the time, and that really spurred me to action as well.” 

Rachel was one of more than 3,600 students to graduate at UL this week, and as a first-time local representative, she said her degree in European Studies has “without a doubt” helped to prepare her for her new role.  

“I could probably go through reams of actual content and papers and academic research that I did, I could give you exact examples that will come into play now in my role, but the main thing is critical thinking. It is the ability to be open minded and have the skills to do your own research, that is the biggest thing. 

“Because there is no guidebook, there is no induction to becoming a councillor, so I'm dealing with queries on housing, medical cards, roads, and footpaths, it's a broad range of issues and I've just started, so having the skills to be able to research properly and effectively and efficiently is huge and I genuinely wouldn't be able to do what I'm doing now had I not learned those really important research skills in UL. 

“Obviously studying politics comes into it but in terms of the other subjects I studied, my time studying marketing was hugely helpful, particularly in planning the campaign. 

“It was massively beneficial to have an understanding of consumer culture and behavior and being able to approach social media strategically, not just throwing something up for the sake of it. And they're all skills and habits that I picked up in my time as a student in UL.” 

Commenting on the landscape for a young woman running an election campaign, Cllr Hartigan said: “I did get a lot of ‘Oh you'll get elected because you're a woman, so you'll get the woman vote or you'll get elected because you're a young person.’ 

“I got elected because I worked my ass off, that's why I got elected.  

“There wasn't an army of young people or an army of women heading to the polling station for me, that's just not what happened, as much as that would have been really cool to see. 

“I got elected because I was canvassing for six or seven hours a day, I was on top of my social media, I was planning and hosting public meetings. 

“I was doing all of the things that you need to do to win, but I was working twice as hard as the average candidate because I had a lot to prove because I am a young woman, so it doesn't make it easier to run as a woman or a young person like some people suggest. 

“You actually have to prove yourself twice as much, and that's not fair, but I think the only way that that will change is if we get more women in and more young people in.” 

Cllr Hartigan credits the support of her family and lecturers in helping her throughout her election campaign.  

“My lecturer Dr Scott Fitzsimmons was very supportive, as was course director Dr Xosé Boan, who advised me to watch myself and my own mental health and well-being as well.  

“Sometimes you get these grand ideas and you're just all go, all the time and you forget to take the time to mind yourself, so I was really glad to have been told that.” 

Rachel does not hail from a political family, with her mother Rosaleen working as a medical secretary and her father Paul the Chief Information Officer for Electric Ireland Superhomes. However, that did not stop Paul from taking on the role of campaign manager.  

“We both learned together and he came out with me every single night, as did my mom. I could not have done it without them,” explained Rachel. 

“Obviously, the focus and the attention is on the candidate, but behind the scenes nobody does it alone, your family has to be on board.  

“It's a huge, massive team effort and for all the work that I was doing with my final year in UL and campaigning, they were out canvassing with me just as much, spending just as many hours at the doors.” 

A native of Parteen, Co. Clare, Rachel attended Parteen National School and now represents the Shannon Electoral Area.  

Reflecting on achieving the two major milestones of graduating university and winning her first election, she said it hasn’t fully sunk in yet. 

“I really felt like I was a campaign/Final Year Project robot, and it’s only the last few weeks I've had time to sit and process and reflect. 

“You never really look at your own accomplishments and achievements and say ‘oh my God, that was really good’. I think Irish people in particular, and women too, are really bad at giving themselves a pat on the back, even when it's well deserved. 

“I'm trying to take in the huge accomplishment, but it's hard to come out and say that and to even feel it, so that's something I'm working on at the moment, giving myself a little pat on the back.”