Examining the Impact of Cognitive and Emotional Factors on Consumer Behavioral Responses in Online Behavioral Advertising

Document Type : Research Paper

Authors

1 Associate Prof., Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran.

2 Assistant Prof., Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran.

3 MSc., Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran.

Abstract

Objective
With the rapid advancement of technology, it has become possible to track consumers' behavior on the Internet. Consequently, marketers can tailor advertisements to align with consumers' needs and characteristics. However, this raises concerns about consumer privacy. Some researchers have concentrated on the positive aspects of Online Behavioral Advertising (OBA), while others have delved into its negative implications. Therefore, this study seeks to examine the impact of cognitive and affective factors on consumers' behavioral responses to OBA. It aims to explore both the positive and negative facets of this advertising approach.
 
Methodology
This survey research adopts an applied approach and targets Internet users from Tehran Universities as the study population. A total of 385 questionnaires were distributed among the sample members. To identify individuals with experience in viewing online behavioral advertisements, a screening question was placed at the beginning of the questionnaire. Of the respondents, 299 reported having seen OBA in the past six months during the survey, and subsequently answered the questions. Descriptive statistics were conducted using SPSS 26, while Smart PLS3 software was employed for inferential statistics.
 
Findings
The findings reveal that perceived personalization positively influences perceived relevance, diminishes perceived intrusiveness, and enhances behavioral responses to advertisements. Additionally, information control demonstrates a negative impact on perceived intrusiveness and privacy concerns, while persuasion knowledge exhibits a positive correlation with privacy concerns. Consumers possessing high levels of persuasion knowledge express increased privacy concerns regarding OBA. Mitigating privacy concerns leads to improved attitudes toward OBA, subsequently enhancing behavioral responses to advertisements. However, privacy concerns do not directly impact behavioral responses to advertisements. Moreover, perceived relevance and perceived intrusiveness serve as mediators in the relationship between perceived personalization and behavioral responses to advertisements.
 
Conclusion
OBA, due to its relevance to the consumer's needs and the reduction of perceived intrusiveness, increases the response to advertisements (a direct and indirect positive mechanism, respectively). This method of advertising is a suitable approach to increase the response rate to online advertisements. Allowing users to control their information will reduce perceived intrusiveness and privacy concerns. Improving consumers' persuasion knowledge by emphasizing the benefits of OBA will reduce their privacy concerns and improve their attitudes toward it, ultimately increasing behavioral responses to the advertisements. In contrast to some past research, privacy concerns do not affect behavioral responses; this may be because of the nature of OBA, which uses different data compared to personalized advertising. Also, OBA is known as a double-edged sword, but the effect of the negative side (privacy concern) is less than the positive side (perceived relevance). This research will contribute to the literature on personalized advertising, specifically online behavioral advertising, and include practical implications for marketers, brand managers, and advertising agencies.

Keywords

Main Subjects

References

 
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. https://doi.org/10.1016/J.JRETAI.2014.09.005
Ahmadi, A. & Ahmadi, D. (2020). Effective factors on increasing the click rate and user reliability In online personalized advertising. Journal of International Business Administration, 4(1), 91-110. (in Persian)
Aiolfi, S., Bellini, S. & Pellegrini, D. (2021). Data-driven digital advertising: benefits and risks of online behavioral advertising. International Journal of Retail and Distribution Management, 49(7), 1089–1110.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Ansari, A. & Mela, C. F. (2018). E-Customization. Journal of Marketing Research,  40(2), 131–145. https://doi.org/10.1509/JMKR.40.2.131.19224
Azizinia, M., Ebrahimzadeh, R. & Sadeghi, M. (2020). The Conceptual Model of Personalized Advertising: A Meta-Synthesis Approach. New Marketing Research Journal, 11(3), 175-196. (in Persian)
Baek, T. H. & Morimoto, M. (2012). Stay Away From Me. Journal of Advertising, 41(1), 59–76. https://doi.org/10.2753/JOA0091-3367410105
Bang, H., Choi, D., Wojdynski, B. W. & Lee, Y. I. (2019). How the level of personalization affects the effectiveness of personalized ad messages: the moderating role of narcissism. International Journal of Advertising, 38(8), 1116–1138.
Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of advertising, 46(3), 363-376.
Brinson, N. H. & Eastin, M. S. (2016). Juxtaposing the persuasion knowledge model and privacy paradox: An experimental look at advertising personalization, public policy and public understanding. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 10(1). https://doi.org/10.5817/CP2016-1-7
Celsi, R. L. & Olson, J. C. (1988). The role of involvement in attention and comprehension processes. Journal of consumer research, 15(2), 210-224.
Ching, R. K. H., Tong, P., Chen, J. S. & Chen, H. Y. (2013). Narrative online advertising: Identification and its effects on attitude toward a product. Internet Research, 23(4), 414–438. https://doi.org/10.1108/INTR-04-2012-0077/FULL/XML
De Keyzer, F., Dens, N. & De Pelsmacker, P. (2021). How and When Personalized Advertising Leads to Brand Attitude, Click, and WOM Intention. Journal of Advertising, 1–18. https://doi.org/10.1080/00913367.2021.1888339
Dodoo, N. A. & Wen, J. (Taylor). (2019). A Path to Mitigating SNS Ad Avoidance: Tailoring Messages to Individual Personality Traits. Journal of Interactive Advertising, 19(2), 116–132. https://doi.org/10.1080/15252019.2019.1573159
Edwards, S. M., Li, H. & Lee, J. H. (2002). Forced Exposure and Psychological Reactance: Antecedents and Consequences of the Perceived Intrusiveness of Pop-Up Ads. Journal of Advertising, 31(3), 83–95. https://doi.org/10.1080/00913367.2002.10673678
Eisend, M. & Tarrahi, F. (2022). Persuasion Knowledge in the Marketplace: A Meta-Analysis. Journal of Consumer Psychology, 32(1), 3–22. https://doi.org/10.1002/JCPY.1258
Friestad, M. & Wright, P. (1994). The Persuasion Knowledge Model: How People Cope with Persuasion Attempts. Journal of Consumer Research, 21(1), 1.
Garson, G.D. (2016). Partial Least Squares: Regression and Structural Equation Models. Statistical Associates Publishers, Asheboro.
Ghanbarpour, T., Sahabeh, E. & Gustafsson, A. (2022). Consumer response to online behavioral advertising in a social media context: The role of perceived ad complicity. Psychology & Marketing, 39(10), 1853-1870.
Gironda, J. T. & Korgaonkar, P. K. (2018). iSpy? Tailored versus Invasive Ads and Consumers’ Perceptions of Personalized Advertising. Electronic Commerce Research and Applications, 29, 64–77. https://doi.org/10.1016/J.ELERAP.2018.03.007
Golalizadeh, F., Ranjbarian, B. & Ansari, A. (2023). Designing a Model for Customer’s Emotions Impact on Online Purchase Intention and impulsive buying of Luxury Cosmetic Products with Emphasis on the Role of Perceived Service Quality. Journal of Business Management, 15(1), 131-155 doi: 10.22059/jibm.2022.334677.4259 (in Persian)
Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks. Sage, 165.
Hair, J. F., Sarstedt, M., Ringle, C. M. & Mena, J. A. (2011). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/S11747-011-0261-6
Ham, C.D. (2017). Exploring how consumers cope with online behavioral advertising. International Journal of Advertising, 36(4), 632-658.
Hasanpour Delavar, M. & Valipour, A. (2020). The effect of emotional factors on customers’ behavioral responses to personalized Internet advertising by Mediating role of rational choice theory components. Journal of New Research Approaches in Management and Accounting, 4(14), 134-156. (in Persian)
Haselimorad, R. & Zandi, R. (2021). Understanding consumer response to online personalized advertising: A new design of rational choice from the perspective of negative effects (case study: Sepideh Mahan Kish Company). Journal of New Research Approaches in Management and Accounting, 5(18), 27-38. (in Persian)
Hayes, J. L., Brinson, N. H., Bott, G. J. & Moeller, C. M. (2021). The Influence of Consumer–Brand Relationship on the Personalized Advertising Privacy Calculus in Social Media. Journal of Interactive Marketing, 55, 16–30.
Henseler, J. (2017). Partial Least Squares Path Modeling. In: Leeflang, P., Wieringa, J., Bijmolt, T., Pauwels, K. (eds) Advanced Methods for Modeling Markets. International Series in Quantitative Marketing. Springer, Cham. https://doi.org/10.1007/978-3-319-53469-5_12
Henseler, J., Ringle, C. M. & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014/FULL/XML
Jackson, D. L. (2003). Revisiting Sample Size and Number of Parameter Estimates: Some Support for the N:q Hypothesis. Structural Equation Modeling: A Multidisciplinary Journal, 10(1), 128–141. https://doi.org/10.1207/S15328007SEM1001_6
Jain, S. & Purohit, H.C. (2022). Privacy concerns and avoidance behaviour towards data-driven online behavioural advertising. Business Analyst Journal, 43(1), 1-12. https://doi.org/10.1108/BAJ-08-2022-0024
Jing Wen, T., Kim, E., Wu, L., & Dodoo, N. A. (2020). Activating persuasion knowledge in native advertising: the influence of cognitive load and disclosure language. International Journal of Advertising, 39(1), 74–93. https://doi.org/10.1080/02650487.2019.1585649
Jung, A. R. (2017). The influence of perceived ad relevance on social media advertising: An empirical examination of a mediating role of privacy concern. Computers in Human Behavior, 70, 303–309. https://doi.org/10.1016/J.CHB.2017.01.008
Jung, J., Shim, S. W., Jin, H. S., & Khang, H. (2016). Factors affecting attitudes and behavioural intention towards social networking advertising: a case of Facebook users in South Korea. International Journal of Advertising, 35(2), 248–265. https://doi.org/10.1080/02650487.2015.1014777
De Keyzer, F., Dens, N., & De Pelsmacker, P. (2015). Is this for me? How Consumers Respond to Personalized Advertising on Social Network Sites. Journal of Interactive Advertising, 15(2), 124–134. https://doi.org/10.1080/15252019.2015.1082450
Kim, H. & Huh, J. (2017). Perceived relevance and privacy concern regarding online behavioral advertising (OBA) and their role in consumer responses. Journal of Current Issues & Research in Advertising, 38(1), 92-105.
Kim, Y. J. & Han, J. (2014). Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization. Computers in Human Behavior, 33, 256–269. https://doi.org/10.1016/J.CHB.2014.01.015
Li, H., Edwards, S. M., & Lee, J. H. (2002). Measuring the intrusiveness of advertisements: Scale development and validation. Journal of advertising, 31(2), 37-47.
MacKenzie, S. B., Lutz, R. J. & Belch, G. E. (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. https://doi.org/10.2307/3151660
Madhavan, V., & George, S. (2020). Perceived Intrusiveness In Digital Advertising: Literature Review And Research Agenda. International Journal of Management, 11(12). https://doi.org/10.34218/IJM.11.12.2020.177
McCoy, S., Everard, A., Galletta, D. F. & Moody, G. D. (2017). Here we go again! The impact of website ad repetition on recall, intrusiveness, attitudes, and site revisit intentions. Information & Management, 54(1), 14–24. https://doi.org/10.1016/J.IM.2016.03.005
Morimoto, M. (2021). Privacy concerns about personalized advertising across multiple social media platforms in Japan: the relationship with information control and persuasion knowledge. International Journal of Advertising, 40(3), 431–451. https://doi.org/10.1080/02650487.2020.1796322
Mpinganjira, M. & Maduku, D. K. (2019). Ethics of mobile behavioral advertising: Antecedents and outcomes of perceived ethical value of advertised brands. Journal of Business Research, 95, 464–478. https://doi.org/10.1016/J.JBUSRES.2018.07.037
Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modelling, Helping researchers discuss more sophisticated models. Industrial Management and Data Systems, 116(9), 1849–1864.
Ozcelik, A. B. & Varnali, K. (2019). Effectiveness of online behavioral targeting: A psychological perspective. Electronic Commerce Research and Applications, 33, 100819. https://doi.org/10.1016/J.ELERAP.2018.11.006
Palanisamy, R. (2014). The impact of privacy concerns on trust, attitude and intention of using a search engine: an empirical analysis. International Journal of Electronic Business, 11(3), 274. https://doi.org/10.1504/IJEB.2014.063032
Phelps, J., Nowak, G., & Ferrell, E. (2018). Privacy Concerns and Consumer Willingness to Provide Personal Information. Journal of Public Policy & Marketing, 19(1), 27–41. https://doi.org/10.1509/JPPM.19.1.27.16941
Pfiffelmann, J., Dens, N. & Soulez, S. (2020). Personalized advertisements with integration of names and photographs: An eye-tracking experiment. Journal of Business Research, 111, 196–207. https://doi.org/10.1016/J.JBUSRES.2019.08.017
Rimer, B. K. & Kreuter, M. W. (2006). Advancing Tailored Health Communication: A Persuasion and Message Effects Perspective. Journal of Communication, 56(SUPPL.), S184–S201. https://doi.org/10.1111/J.1460-2466.2006.00289.X
Rastegari, R., Ebrahimi, A. & Amini, A. (2022). Providing a Model of Mental Shortcuts of Influential Users for Choosing Marketing Influencers on Instagram. Journal of Business Management, 14(4), 602-624. doi: 10.22059/jibm.2022.336226.4280 (in Persian)
Roshandel Arbatani, T. & Mahmoudzadeh, A. (2018). Advertising through Social Media to Influence the Customers’ Willing. Journal of Business Management, 9(4), 736-786. doi: 10.22059/jibm.2017.226498.2471 (in Persian)
Segijn, C. M., & Van Ooijen, I. (2022). Differences in consumer knowledge and perceptions of personalized advertising: Comparing online behavioural advertising and synced advertising. Journal of Marketing Communications, 28(2), 207-226.
Sheehan, K. B. (2011). Toward a Typology of Internet Users and Online Privacy Concerns. The information society,18(1), 21–32. https://doi.org/10.1080/01972240252818207
Spielmann, N. & Richard, M.-O. (2013). How captive is your audience? Defining overall advertising involvement. Journal of Business Research, 66(4), 499–505. https://doi.org/https://doi.org/10.1016/j.jbusres.2011.12.002
Statista. (2021). Statista. https://www.statista.com/statistics/242552/digital-advertising-spending-in-the-us/
Taylor, D. G., Davis, D. F. & Jillapalli, R. (2009). Privacy concern and online personalization: The moderating effects of information control and compensation. Electronic Commerce Research, 9(3), 203–223. https://doi.org/10.1007/S10660-009-9036-2
Tran, T. P. (2017). Personalized ads on Facebook: An effective marketing tool for online marketers. Journal of Retailing and Consumer Services, 39, 230–242. https://doi.org/10.1016/J.JRETCONSER.2017.06.010
Tran, T. P., Muldrow, A. & Ho, K. N. B. (2021). Understanding drivers of brand love - the role of personalized ads on social media. Journal of Consumer Marketing, 38(1), 1–14. https://doi.org/10.1108/JCM-07-2019-3304/FULL/XML
van Doorn, J. & Hoekstra, J. C. (2013). Customization of online advertising: The role of intrusiveness. Marketing Letters,  24(4), 339–351. https://doi.org/10.1007/S11002-012-9222-1
Varnali, K. (2021). Online behavioral advertising: An integrative review. Journal of Marketing Communications, 27(1), 93–114. https://doi.org/10.1080/13527266.2019.1630664
Wang, Y. & Sun, S. (2010). Assessing beliefs, attitudes, and behavioral responses toward online advertising in three countries. International Business Review, 19(4), 333–344. https://doi.org/10.1016/J.IBUSREV.2010.01.004
Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly: Management Information Systems, 33(1), 177–196. https://doi.org/10.2307/20650284
Wheeler, S. C., Petty, R. E. & Bizer, G. Y. (2005). Self-schema matching and attitude change: Situational and dispositional determinants of message elaboration. Journal of Consumer Research, 31(4), 787–797. https://doi.org/10.1086/426613/0
White, T. B., Zahay, D. L., Thorbjørnsen, H., & Shavitt, S. (2007). Getting too personal: Reactance to highly personalized email solicitations. Marketing Letters, 19(1), 39–50. https://doi.org/10.1007/S11002-007-9027-9
Xie, W. & Karan, K. (2019). Consumers’ Privacy Concern and Privacy Protection on Social Network Sites in the Era of Big Data: Empirical Evidence from College Students. Journal of Interactive Advertising, 19(3), 187–201.
Youn, S. & Kim, S. (2019). Understanding ad avoidance on Facebook: Antecedents and outcomes of psychological reactance. Computers in Human Behavior, 98, 232–244. https://doi.org/10.1016/J.CHB.2019.04.025
Zamani, H., Naami, A. & Hamdi, K. (2022). Designing a Content Marketing Template to Increase Purchase Intention in Digital Marketing. Journal of Business Management, 14(2), 354-376. doi: 10.22059/jibm.2021.332652.4222 (in Persian)
Zarouali, B., Poels, K., Walrave, M., & Ponnet, K. (2018). ‘You talking to me?’ The influence of peer communication on adolescents’ persuasion knowledge and attitude towards social advertisements. Behaviour & Information Technology, 37(5), 502–516.
Zarouali, B., Ponnet, K., Walrave, M. & Poels, K. (2017). “Do you like cookies?” Adolescents’ skeptical processing of retargeted Facebook-ads and the moderating role of privacy concern and a textual debriefing. Computers in Human Behavior, 69, 157–165. https://doi.org/10.1016/J.CHB.2016.11.050
Zhang, J. & Mao, E. (2016). From Online Motivations to Ad Clicks and to Behavioral Intentions: An Empirical Study of Consumer Response to Social Media Advertising. Psychology & Marketing, 33(3), 155–164. https://doi.org/10.1002/MAR.20862
Journal of Business Management
Volume 16, Issue 1
2024
Pages 1-33

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