The Role of Big Data-Driven Targeted Advertising in Shaping Brand Attitude and Purchase Intentions Among Digital Consumers
DOI:
https://doi.org/10.66635/qfn5es35Keywords:
Big Data-Driven Marketing, Targeted Advertising, Personalized Advertising, Advertisement Relevance, Brand Attitude, Purchase Intention, Digital Consumers, Algorithmic Targeting, Consumer Behaviour, Data-Driven Marketing StrategiesAbstract
Big data-driven targeted advertising has transformed contemporary digital marketing by enabling firms to deliver hyper-personalized and contextually relevant promotional content. Despite its widespread adoption, the psychological mechanisms through which data-driven targeting shapes brand evaluation and consumer behaviour remain insufficiently understood. Drawing on the Stimulus–Organism–Response (S–O–R) framework, this study investigates how big data-driven targeted advertising (BDTA) influences purchase intention (PI) through perceived personalization (PP), advertisement relevance (AR), and brand attitude (BA). Using survey data from 326 digital consumers and applying Partial Least Squares Structural Equation Modelling (PLS-SEM), the study reveals that BDTA exerts strong positive effects on both PP and AR, demonstrating its capacity to reshape consumers’ perceptual and cognitive interpretations of digital advertisements. PP and AR significantly enhance BA, with personalization exhibiting a slightly stronger effect, underscoring the emotional salience of individualized messaging. BA emerges as the most powerful predictor of PI, mediating the influence of both personalization and relevance on behavioural intention. The findings highlight a coherent multi-stage persuasion pathway in which algorithmic targeting functions as the stimulus, perceptual evaluations serve as organismic states, and attitude and intention constitute the response. By elucidating these mechanisms, the study advances theoretical understanding of algorithmic persuasion and offers practical insights for marketers seeking to optimise data-driven advertising strategies. The results underscore the need for brands to balance technological precision with psychologically meaningful personalization to cultivate favourable attitudes and drive purchase behaviour.
References
1.D, B. (2014). It’s complicated. Yale University Press.
2.A.E., M. (2015). Instafame. . Public Culture, 27(1),, 137–160.
3.C., A. (2016). Aren’t these just young, rich women doing vain things online? Social Media + Society, 2(2), , 1–17.
4.van Dijck, J., & Poell, T. (2013). Understanding social media logic. . Media and Communication, 1(1),, 2–14.
5.Gillespie, T. (2010). The politics of platforms. New Media & Society, 12(3),, 347–364.
6.Helmond, A. (2015). The platformization of the web. New Media & Society, 18(9),, 1–17.
7.van Dijck, J., Poell, T., & Waal, M. (2018). The Platform Society: Public values in a connective world. Oxford University Press.
8.Plantin, J.-C., Lagoze, C., Edwards, P., & Sandvig, C. (2018). Infrastructure meets platform studies. New Media & Society, 20(1), , 293–310.
9.Nieborg, D., & Poell, T. (2018). The platformization of cultural production. . New Media & Society, 20(11), , 4275–4292.
10.Bakshy, E., Messing, S., & Adamic, L. (2015). Exposure to ideologically diverse news and opinion on Facebook. . Science, 348(6239), , 1130–1132.
11.Cotter, K. (2019). Playing the visibility game: How Instagram users optimize posts for the algorithm. . New Media & Society, 21(4), , 895–913.
12.Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. . Colorado Technology Law Journal, 13, , 203–218.
13.Bucher, T. (2012). Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. . New Media & Society, 14(7), , 1164–1180.
14.Lomborg, S., & Kapsch, A. (2020). Commercial content moderation on social media: Digital labour, biases and injustices. . Digital Journalism, 8(6), , 756–775.
15.Couldry, N., & van Dijck, J. (2015). Researching social media as if the social mattered. . Social Media + Society, 1(2), , 1–7.
16.Nakamura, L. (2020). Feeling good about being visible: Algorithmic labor in social media. . Feminist Media Studies, 20(8), , 1267–1283.
17.Striphas, T. (2015). Algorithmic culture. . European Journal of Cultural Studies, 18(4–5), , 395–412.
18.Cheney-Lippold, J. (2017). We are data: Algorithms and the making of digital selves. . New York University Press.
19.Kitchin, R. (2017). Thinking critically about algorithms. . Information, Communication & Society, 20(1), , 14–29.
20.Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. . Harvard University Press.
21.Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
22.Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., . . . Sandvig, C. (2015). “I always assumed that I wasn’t really that close to [the algorithm]”: User understanding of Facebook’s algorithmic curation. CHI ’15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (, (pp. pp. 153–162)).
23.Srnicek, N. (2017). Platform capitalism. . Polity Press.
24.Abidin, C. (2021). Mapping Internet celebrity on TikTok: Exploring attention economies and visibility labours. . Social Media + Society, 7(1), , 1–12.
25.Duffy, B. (2017). Not getting paid to do what you love: Gender, social media, and aspirational work. . Yale University Press.
26.Bishop, S. (2020). Algorithmic experts: Selling algorithmic lore on YouTube. . Social Media + Society, 6(2),, 1–12.
27.Rettberg, J. (2014). Seeing ourselves through technology: How we use selfies, blogs and wearable devices to see and shape ourselves. . Palgrave Macmillan.
28.Nayar, P. (2011). Seeing stars: Spectacle, society and celebrity culture. . Sage Publications.
29.Miller, D., Costa, E., Haynes, N., McDonald, T., Nicolescu, R., Sinanan, J., . . . Wang, X. (2016). How the world changed social media. . UCL Press.
30.Chaudhuri, M. (2020). Youth identities and digital consumption in India. . Journal of Youth Studies, 23(8), , 1023–1039.
31.Kaur-Gill, S. (2020). Digital identities and youth culture in India: A qualitative study of online self-presentation. . Asian Journal of Communication, 30(5), , 421–438.
32.Arora, P., & Scheiber, L. (2017). Platforms, power, and politics: The “cultural logic” of India’s online publics. . Communication and the Public, 2(4), , 360–378.
33.Udupa, S., & Pohjonen, M. (2019). Extreme speech online: Observing, measuring, and mitigating harmful content. . International Journal of Communication, 13, , 4502–4522.
34.Bucher, T. (2018). If… then: Algorithmic power and politics. . Oxford University Press.
35.Creswell, J., & Plano Clark, V. (2018). Designing and conducting mixed methods research (3rd ed.). . Sage Publications.
36.Bryman, A. (2016). Social research methods (5th ed.). . Oxford University Press.
37.de Jans, S., Cauberghe, V., & Hudders, L. (2020). How social media influencers persuade adolescents: The role of followers’ online parental monitoring. . International Journal of Advertising, 39(3), , 1–27.
38.Kline, R. (2015). Principles and practice of structural equation modeling (4th ed.). . Guilford Press.
39.Denzin, N., & Lincoln, Y. (2018). The Sage handbook of qualitative research (5th ed.). . Sage Publications.
40.Banaji, S., & Buckingham, D. (2013). The civic web: Young people, the Internet and civic participation. . MIT Press.
41.Guest, G., Bunce, a., & Johnson, l. (2006). How many interviews are enough? An experiment with data saturation and variability. . Field Methods, 18(1), 59–82.
42.Abidin, C. (2016). “Aren’t these just young, rich women doing vain things online?” Influencer selfies as subversive frivolity. . Social Media + Society, 2(2), , 1–17.
43.Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt.
44.Stone, M., & Woodcock, N. (2014). Interactive, direct and digital marketing: A future that depends on better use of business intelligence. Journal of Research in Interactive Marketing, 8(1), 4–17.
45.Wedel, M., & Kannan, P. (n.d.). Marketing analytics for data-rich environments. . Journal of Marketing, 80(6), , 97–121.
46.Davenport, T., Barth, P., & Bean, R. (2012). How ‘big data’ is different. . MIT Sloan Management Review, , 54(1), 22–24.
47.Tucker, C. (2014). Social networks, personalized advertising, and privacy controls. . Journal of Marketing Research, 51(5), , 546–562.
48.Bleier, A., & Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. . Marketing Science, 34(5), , 669–688.
49.Arora, A., Hofacker, C., & Stone, M. (2017). Artificial intelligence in marketing: Evolution, challenges, and opportunities. . Journal of Business Research, 80, , 28–37.
50.Lamberton, C., & Stephen, A. (2016). A thematic exploration of digital, social media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. . Journal of Marketing, 80(6), , 146–172.
51.Wedel, M., & Kannan, P. (2016). Marketing analytics for data-rich environments. . Journal of Marketing, 80(6), , 97–121.
52.Shmueli, G., & Koppius, O. (2011). Predictive analytics in information systems research. . MIS Quarterly, 35(3), , 553–572.
53.DeVito, M. (2017). From editors to algorithms: A values-based approach to understanding story selection in the Facebook news feed. . Digital Journalism, 5(6), , 753–773.
54.Zeng, F., Chen, Y., & Chen, P. (2019). The value of big data in digital advertising: Empirical evidence from China. . Journal of Business Research, 98, , 322–333.
55.Rust, R., & Huang, M.-H. (2014). The service revolution and the transformation of marketing science. . Marketing Science, 33(1), , 206–221.
56.Luo, X., Andrews, M., Fang, Z., & Phang, C. (2014). Mobile targeting. . Management Science, 60(7), , 1738–1756.
57.Bart, Y., Stephen, A., & Sarvary, M. (2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. . Journal of Marketing Research, 51(3), , 270–285.
58.Tam, K., & Ho, S. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. . MIS Quarterly, 30(4), , 865–890.
59.Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. . Journal of Retailing, 91(1), , 34–49.
60.Zarouali, B., Ponnet, K., Poels, K., & Walrave, M. (2017). “Do you like cookies?” Adolescents’ skeptical processing of retargeted Facebook advertisements. . Journal of Business Research, 86, , 170–178.
61.Baek, T., & Morimoto, M. (2012). Stay away from me: Examining the determinants of consumer avoidance of personalized advertising. . Journal of Advertising, 41(1), , 59–76.
62.Boerman, S., Kruikemeier, S., & Zuiderveen Borgesius, F. (2017). Online behavioral advertising: A literature review and research agenda. . Journal of Advertising, 46(3), , 363–376.
63.MacKenzie, S., Lutz, R., & Belch, G. (1986). The role of attitude toward the ad as a mediator of advertising effectiveness: A test of competing explanations. . Journal of Marketing Research, 23(2), , 130–143.
64.Keller, K. (2013). Strategic Brand Management: Building, Measuring, and Managing Brand Equity (4th ed.). . Pearson Education.
65.Kim, H., & Huh, J. (2017). The effect of message personalization and source credibility on consumers’ behavioral intention in online advertising. Journal of Interactive Advertising, 17(1), , 59–73.
66.Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
67.Pavlou, P., & Stewart, D. (2000). Measuring the effects and effectiveness of interactive advertising: A research agenda. . Journal of Interactive Advertising, 1(1), , 62–78.
68.Hoffman, D., & Novak, T. (2018). Consumer and object experience in the internet of things: An assemblage theory approach. . Journal of Consumer Research, 44(6), , 1178–1204.
69.Shankar, V., Söllner, M., Adam, M., Benlian, A., Hann, I.-H., Richter, A., & Rowe, F. (2021). Algorithmic decision-making and the future of marketing. Journal of the Academy of Marketing Science, 49(4), 659–680. Journal of the Academy of Marketing Science, 49(4), , 659–680.
70.Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: From big data to big impact. . MIS Quarterly, 36(4), , 1165–1188.
71.White, K., Habib, R., & Hardisty, D. (2019). How to SHIFT consumer behaviors to be more sustainable: A literature review and guiding framework. . Journal of Marketing, 83(3), , 22–49.
72.Ur, B., & Wang, Y. (n.d.). A field study of privacy perceptions and behaviors of users of interest-based advertising. . In Proceedings of the 9th Symposium on Usable Privacy and Security (SOUPS) (, (pp. 1-15). 2013.
73.Lwin, M., Phau, I., & Lim, A. (2014). Examining the role of attitudinal ambivalence in customer–brand relationships. . Marketing Intelligence & Planning, 32(3), , 275–292.
74.Ham, C.-D. (2021). The role of personalization in the sharing of persuasive messages: Examining the effects of personalized advertisements on consumer responses. 180–198.
75.Malthouse, E., & Li, H. (2017). Opportunities for and pitfalls of using big data in advertising research. . Journal of Advertising Research, 57(3), , 227–231.
76.Punj, G. (2021). Consumer decision-making: A review of research findings, theories, and models. . Journal of Retailing and Consumer Services, 61, , 102540.
77.Susser, D., Roessler, B., & Nissenbaum, H. (2019). Online manipulation: Hidden influences in a digital world. . Georgetown Law Technology Review, 3(2), , 1–45.
78.Yeung, K. (2017). ‘Hypernudge’: Big data as a mode of regulation by design. . Information, Communication & Society, 20(1), , 118–136.
79.Mehrabian, A., & Russell, J. (1974). An Approach to Environmental Psychology. . MIT Press.
80.Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). . Sage Publications.
81.Jarvis, C., MacKenzie, S., & Podsakoff, P. (2003). A critical review of construct indicators and measurement model specification in marketing and consumer research. . Journal of Consumer Research, 30(2), , 199–218.
82.Dillman, D., Smyth, J., & Christian, L. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). . Wiley.
83.Henseler, J., Ringle, C., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. . Journal of the Academy of Marketing Science, 43(1), , 115–135.
84.Tenenhaus, M., Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. . Computational Statistics & Data Analysis, 48(1), , 159–205.
85.Hair, J., Risher, J., Sarstedt, M., & Ringle, C. (2021). When to use and how to report the results of PLS-SEM. . European Business Review, 33(1), , 2–24.
86.Preacher, K., & Hayes, A. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. . Behavior Research Methods, 40(3), , 879–891.
87.Petty, R., & Cacioppo, J. (1986). Communication and Persuasion: Central and Peripheral Routes to Attitude Change. . Springer.
