A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps
Research on mobile advertising has evolved considerably over the last few years. However, several under-researched topics remain, including mobile advertising spreading by leveraging users’ personal social networks. The present research addresses this gap by proposing a model for studying advertisin...
- Autores:
-
García-Dávalos, Alexander
García-Duque, Jorge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2023
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/15530
- Acceso en línea:
- https://hdl.handle.net/10614/15530
https://doi.org/10.1080/10496491.2023.2189204
https://red.uao.edu.co/
- Palabra clave:
- Mobile advertising
Advertising spreading
Branded apps
Personalsocial networks
Viraladvertising
User privacy perception
- Rights
- openAccess
- License
- Derechos reservados - Taylor y Francis Group, 2023
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A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps |
| title |
A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps |
| spellingShingle |
A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps Mobile advertising Advertising spreading Branded apps Personalsocial networks Viraladvertising User privacy perception |
| title_short |
A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps |
| title_full |
A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps |
| title_fullStr |
A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps |
| title_full_unstemmed |
A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps |
| title_sort |
A simplified mobile advertising model to study advertising spreading through personal social networks and branded apps |
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García-Dávalos, Alexander García-Duque, Jorge |
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García-Dávalos, Alexander García-Duque, Jorge |
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Taylor & Francis Group |
| dc.subject.proposal.eng.fl_str_mv |
Mobile advertising Advertising spreading Branded apps Personalsocial networks Viraladvertising User privacy perception |
| topic |
Mobile advertising Advertising spreading Branded apps Personalsocial networks Viraladvertising User privacy perception |
| description |
Research on mobile advertising has evolved considerably over the last few years. However, several under-researched topics remain, including mobile advertising spreading by leveraging users’ personal social networks. The present research addresses this gap by proposing a model for studying advertising spreading through personal networks using a viral approach and branded apps. The proposed model simulation suggests that the influence of spreader users’ percentage in ad spreading was significant, but increasing it beyond 20% did not impact the spreading growth. Additionally, including user privacy perception in the model positively affected spreading by increasing advertising propagation risk |
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2024-04-12T20:07:40Z |
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García-Dávalos, A.; García-Duque, J. (2023). A Simplified Mobile Advertising Model to Study Advertising Spreading through Personal Social Networks and Branded Apps. Journal of Promotion Management. 29(8). p.p. 1104-1137. https://doi.org/10.1080/10496491.2023.2189204 |
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https://doi.org/10.1080/10496491.2023.2189204 |
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Universidad Autónoma de Occidente |
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Respositorio Educativo Digital UAO |
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García-Dávalos, A.; García-Duque, J. (2023). A Simplified Mobile Advertising Model to Study Advertising Spreading through Personal Social Networks and Branded Apps. Journal of Promotion Management. 29(8). p.p. 1104-1137. https://doi.org/10.1080/10496491.2023.2189204 1049-6491 Universidad Autónoma de Occidente Respositorio Educativo Digital UAO |
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Journal of Promotion Management |
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Adam, D. C., Wu, P., Wong, J. Y., Lau, E. H. Y., Tsang, T. K., Cauchemez, S., Leung, G. M., & Cowling, B. J. (2020). Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nature Medicine, 26(11), 1714–1719. https://doi.org/10.1038/ s41591-020-1092-0 Akpojivi, U., & Bevan-Dye, A. (2015). Mobile advertisements and information privacy perception amongst South African Generation Y students. Telematics and Informatics, 32(1), 1–10. https://doi.org/10.1016/j.tele.2014.08.001 Alsuwaidan, L., & Ykhlef, M. (2016). Toward information diffusion model for viral marketing in business. International Journal of Advanced Computer Science and Applications, 7(2), 637–646. https://doi.org/10.14569/IJACSA.2016.070280 AMA. (2021, October 11). Statement of ethics. https://myama.my.site.com/s/article/AMAStatement- of-Ethics Arnaboldi, V., Conti, M., Passarella, A., & Dunbar, R. I. M. (2017). Online social networks and information diffusion: The role of ego networks. Online Social Networks and Media, 1, 44–55. https://doi.org/10.1016/j.osnem.2017.04.001 Atdag, S., & Bingol, H. O. (2021). Computational models for commercial advertisements in social networks. Physica A: Statistical Mechanics and Its Applications, 572, 125916. https://doi.org/10.1016/j.physa.2021.125916 Baek, T. H., & Morimoto, M. (2012). Stay away from me. Journal of Advertising, 41(1), 59– 76. https://doi.org/10.2753/JOA0091-3367410105 Bakhshandeh, R., Samadi, M., Azimifar, Z., Schaeffer, J. (2011). Degrees of separation in social networks. Proceedings of the International Symposium on Combinatorial Search, 2(1), 18–23. https://doi.org/10.1609/socs.v2i1.18200 Banerjee, S., Jenamani, M., & Pratihar, D. K. (2020). A survey on influence maximization in a social network. Knowledge and Information Systems, 62(9), 3417–3455. https://doi. org/10.1007/s10115-020-01461-4 Barutcu, S., Yaldir, A., & Hasiloglu, S. B. (2017). From mass to personalized mobile marketing strategies: The new dimensions through expert systems. European Scientific Journal, 13(10), 400–409. https://doi.org/10.19044/esj.2017.v13n10p%p Bellman, S., Potter, R. F., Treleaven-Hassard, S., Robinson, J. A., & Varan, D. (2011). The effectiveness of branded mobile phone apps. Journal of Interactive Marketing, 25(4), 191– 200. https://doi.org/10.1016/j.intmar.2011.06.001 Bhattacharya, S., Gaurav, K., & Ghosh, S. (2019). Viral marketing on social networks: An epidemiological perspective. Physica A: Statistical Mechanics and Its Applications, 525, 478–490. https://doi.org/10.1016/j.physa.2019.03.008 Boerman, S. C., & Smit, E. G. (2023). Advertising and privacy: An overview of past research and a research agenda. International Journal of Advertising, 42(1), 60–68. https://doi.org/10.1080/02650487.2022.2122251 Briskman, J. (2019, April 29). Top sneaker apps in the U.S. from 2015 to 2018. Sensor Tower Blog. https://sensortower.com/blog/top-sneaker-apps-us-2015-2018 Carroll, C., Bhattacharjee, S., Chen, Y., Dubey, P., Fan, J., Gajardo, A., Zhou, X., M€uller, H.-G., & Wang, J. L. (2020). Time dynamics of COVID-19. Scientific Reports, 10(1), 1– 14. https://doi.org/10.1038/s41598-020-77709-4 CDC. (2021, December 20). Principles of epidemiology j Lesson 3—Section 2. https://www. cdc.gov/csels/dsepd/ss1978/lesson3/section2.html Chen, Z. (2019). An agent-based model for information diffusion over online social networks. Papers in Applied Geography, 5(1–2), 77–97. https://doi.org/10.1080/23754931.2019.1619193 Chu, S.-C., & Kim, J. (2018). The current state of knowledge on electronic word-of-mouth in advertising research. International Journal of Advertising, 37(1), 1–13. https://doi.org/ 10.1080/02650487.2017.1407061 de Souza, R. C., Figueiredo, D. R., de, A., Rocha, A. A., & Ziviani, A. (2020). Efficient network seeding under variable node cost and limited budget for social networks. Information Sciences, 514, 369–384. https://doi.org/10.1016/j.ins.2019.11.029 Degirmenci, K. (2020). Mobile users’ information privacy concerns and the role of app permission requests. International Journal of Information Management, 50, 261–272. https:// doi.org/10.1016/j.ijinfomgt.2019.05.010 Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021). Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. Journal of Business Research, 135, 758–773. https://doi.org/10.1016/j.jbusres. 2021.07.015 Eckler, P., & Bolls, P. (2011). Spreading the virus: Emotional tone of viral advertising and its effect on forwarding intentions and attitudes. Journal of Interactive Advertising, 11(2), 1–11. https://doi.org/10.1080/15252019.2011.10722180 Endo, A., Abbott, S., Kucharski, A. J., & Funk, S. (2020). Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China (5:67). Wellcome Open Research, 5, 67. https://doi.org/10.12688/wellcomeopenres.15842.3 Furini, M., Mirri, S., Montangero, M., & Prandi, C. (2020). Privacy perception when using smartphone applications. Mobile Networks and Applications, 25(3), 1055–1061. https:// doi.org/10.1007/s11036-020-01529-z Garcia-Davalos, A., & Garcia-Duque, J. (2020). User profile modelling based on mobile phone sensing and call logs. In A. Rocha, C. Ferr as, C. E. Montenegro Marin, & V. H. Medina Garc ıa (Eds.), Information technology and systems (pp. 243–254). Springer International Publishing. https://doi.org/10.1007/978-3-030-40690-5_24 Ghosh, S., Bhattacharya, S., Gaurav, K., & Singh, Y. N. (2018). Going viral: The epidemiological strategy of referral marketing. (arXiv:180803780). arXiv. https://doi.org/10.48550/ arXiv.1808.03780 G€ursakal, N., Batmaz, B., & Aktuna, G. (2020). Drawing transmission graphs for COVID- 19 in the perspective of network science. Epidemiology and Infection, 148, E269. https:// doi.org/10.1017/S0950268820002654 Hayes, J. L., King, K. W., & Ramirez, A. (2016). Brands, friends, & viral advertising: A social exchange perspective on the ad referral processes. Journal of Interactive Marketing, 36, 31–45. https://doi.org/10.1016/j.intmar.2016.04.001 Himelboim, I., & Golan, G. J. (2019). A social networks approach to viral advertising: The role of primary, contextual, and low influencers. Social MediaþSociety, 5(3), 2056305119847516. https://doi.org/10.1177/2056305119847516 Jebarajakirthy, C., Maseeh, H. I., Morshed, Z., Shankar, A., Arli, D., & Pentecost, R. (2021). Mobile advertising: A systematic literature review and future research agenda. International Journal of Consumer Studies, 45(6), 1258–1291. https://doi.org/10.1111/ijcs. 12728 Jing, Y., Peiyu, L., Xiaobing, T., & Wenfeng, L. (2018). Improved SIR advertising spreading model and its effectiveness in social network. Procedia Computer Science, 129, 215–218. https://doi.org/10.1016/j.procs.2018.03.044 Jingyan, L., & Liwen, H. (2018). Mobile advertising optimization strategy based on SICAS model in China. International Journal of Business and Management, 13(11), 169–175. https://doi.org/10.5539/ijbm.v13n11p169 Kelly, L., Kerr, G., Drennan, J., & Fazal-E-Hasan, S. M. (2021). Feel, think, avoid: Testing a new model of advertising avoidance. Journal of Marketing Communications, 27(4), 343– 364. https://doi.org/10.1080/13527266.2019.1666902 Kempe, D., Kleinberg, J., & Tardos, E. (2015). Maximizing the spread of influence through a social network. Theory of Computing, 11(1), 105–147. https://doi.org/10.4086/toc.2015. v011a004 Kim, M., & Fernandez, R. M. (2017). Strength matters: Tie strength as a causal driver of networks’ information benefits. Social Science Research, 65, 268–281. https://doi.org/10. 1016/j.ssresearch.2016.11.003 Kweon, H., Zhang, D., & Zhou, L. (2012). User privacy in mobile advertising. In AMCIS 2012 Proceedings. https://aisel.aisnet.org/amcis2012/proceedings/HCIStudies/1 Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM Transactions on the Web, 1(1), 5-es. https://doi.org/10.1145/1232722.1232727 Leskovec, J., & Horvitz, E. (2008). Planetary-scale views on a large instant-messaging network. In Proceedings of the 17th International Conference on World Wide Web, pp. 915– 924. https://doi.org/10.1145/1367497.1367620 Li, Y., Fan, J., Wang, Y., & Tan, K.-L. (2018). Influence maximization on social graphs: A survey. IEEE Transactions on Knowledge and Data Engineering, 30(10), 1852–1872. https://doi.org/10.1109/TKDE.2018.2807843 Liu, F., Kanso, A., Zhang, Y., & Olaru, D. (2019). Culture, perceived value, and advertising acceptance: A cross-cultural study on mobile advertising. Journal of Promotion Management, 25(7), 1028–1058. https://doi.org/10.1080/10496491.2019.1612495 Li, M., Wang, X., Gao, K., & Zhang, S. (2017). A survey on information diffusion in online social networks: Models and methods. Information, 8(4), 118. https://doi.org/10.3390/ info8040118 Maseeh, H. I., Jebarajakirthy, C., Pentecost, R., Ashaduzzaman, M., Arli, D., & Weaven, S. (2021). A meta-analytic review of mobile advertising research. Journal of Business Research, 136, 33–51. https://doi.org/10.1016/j.jbusres.2021.06.022 Mittal, S., & Kumar, V. (2020). A framework for ethical mobile marketing. International Journal of Technoethics, 11, 28–42. https://doi.org/10.4018/IJT.2020010103 Moldovan, S., Steinhart, Y., & Lehmann, D. R. (2019). Propagators, creativity, and informativeness: What helps ads go viral. Journal of Interactive Marketing, 47(1), 102–114. https://doi.org/10.1016/j.intmar.2019.02.004 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 Nielsen. (2015, September 28). Recommendations from friends remain most credible form of advertising among consumers; Branded websites are the second highest rated form. https:// www.nielsen.com/eu/en/press-releases/2015/recommendations-from-friends-remain-mostcredible- form-of-advertising Palka, W., Pousttchi, K., & Wiedemann, D. G. (2009). Mobile word-of-mouth – A grounded theory of mobile viral marketing. Journal of Information Technology, 24(2), 172–185. https://doi.org/10.1057/jit.2008.37 Razaque, A., Rizvi, S., Khan, M. J., Almiani, M., & Rahayfeh, A. A. (2022). State-of-art review of information diffusion models and their impact on social network vulnerabilities. Journal of King Saud University - Computer and Information Sciences, 34(1), 1275– 1294. https://doi.org/10.1016/j.jksuci.2019.08.008 Rodrigues, H. S., & Fonseca, M. J. (2016). Can information be spread as a virus? Viral marketing as epidemiological model. Mathematical Methods in the Applied Sciences, 39(16), 4780–4786. https://doi.org/10.1002/mma.3783 Salehi, M., Sharma, R., Marzolla, M., Magnani, M., Siyari, P., & Montesi, D. (2015). Spreading processes in multilayer networks. IEEE Transactions on Network Science and Engineering, 2(2), 65–83. https://doi.org/10.1109/TNSE.2015.2425961 Sarin, C., & Pal, B. (2014). Word of mouth marketing: Consumers participation. The International Journal of Business & Management, 2(1), 74–79. http://www.internationaljournalcorner. com/index.php/theijbm/article/view/128400 Segev, S., & Fernandes, J. (2022). The anatomy of viral advertising: A content analysis of viral advertising from the elaboration likelihood model perspective. Journal of Promotion Management, 29(1), 1–30. https://doi.org/10.1080/10496491.2022.2108189 Sen, S., & Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the Web. Journal of Interactive Marketing, 21(4), 76–94. https://doi. org/10.1002/dir.20090 Shaikh, B. (2014). Understanding word of mouth communication: A theoretical review of literature. The International Journal of Business & Management, 2(4), 58–64. http:// www.internationaljournalcorner.com/index.php/theijbm/article/view/127642 Sigurdsson, V., Menon, R. G. V., Hallgr ımsson, A. G., Larsen, N. M., & Fagerstrøm, A. (2018). Factors affecting attitudes and behavioral intentions toward in-app mobile advertisements. Journal of Promotion Management, 24(5), 694–714. https://doi.org/10.1080/ 10496491.2018.1405523 Silva, E., Pereira, F. H., & Schimit, P. H. T. (2020). Information spreading in a population modeled by continuous asynchronous probabilistic cellular automata. Computer Communications, 154, 288–297. https://doi.org/10.1016/j.comcom.2020.02.074 Statista. (2021). Mobile ad blocking penetration U.S. 2020. https://www.statista.com/statistics/ 554304/interest-mobile-ad-blocking/ Statista. (2022). Mobile ad spend worldwide 2024. https://www.statista.com/statistics/303817/ mobile-internet-advertising-revenue-worldwide/ Stocchi, L., Michaelidou, N., & Micevski, M. (2019). Drivers and outcomes of branded mobile app usage intention. Journal of Product & Brand Management, 28(1), 28–49. https://doi.org/10.1108/JPBM-02-2017-1436 Stonedahl, F., & Wilensky, U. (2008). NetLogo models library: Virus on a network. http:// ccl.northwestern.edu/netlogo/models/VirusonaNetwork Tang, A. K. Y. (2019). A systematic literature review and analysis on mobile apps in mcommerce: Implications for future research. Electronic Commerce Research and Applications, 37, 100885. https://doi.org/10.1016/j.elerap.2019.100885 Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 51(5), 546–562. https://doi.org/10.1509/jmr.10.0355 Ullah, I., Boreli, R., Kanhere, S. S., Chawla, S., Ahanger, T. A., & Tariq, U. (2020). Protecting private attributes in app based mobile user profiling. IEEE Access, 8, 143818– 143836. https://doi.org/10.1109/ACCESS.2020.3014424 Ullah, I., Boreli, R., & Kanhere, S. S. (2021). Privacy in targeted advertising: A survey. arXiv preprint. http://arxiv.org/abs/2009.06861 Ullah, I., Kanhere, S. S., & Boreli, R. (2020). Privacy-preserving targeted mobile advertising: A blockchain-based framework for mobile ads. ArXiv:2008.10479 [Cs]. http://arxiv.org/ abs/2008.10479 Um, N.-H. (2019). Predictors of mobile advertising avoidance: What makes people avoid mobile advertising? International Journal of Contents, 15(4), 44–49. https://doi.org/10. 5392/IJoC.2019.15.4.044 van Noort, G., & van Reijmersdal, E. A. (2019). Branded apps: Explaining effects of brands’ mobile phone applications on brand responses. Journal of Interactive Marketing, 45, 16– 26. https://doi.org/10.1016/j.intmar.2018.05.003 Wang, T., Duong, T. D., & Chen, C. C. (2016). Intention to disclose personal information via mobile applications: A privacy calculus perspective. International Journal of Information Management, 36(4), 531–542. https://doi.org/10.1016/j.ijinfomgt.2016.03.003 Wang, Y., Genc, E., & Peng, G. (2020). Aiming the mobile targets in a cross-cultural context: Effects of trust, privacy concerns, and attitude. International Journal of Human– Computer Interaction, 36(3), 227–238. https://doi.org/10.1080/10447318.2019.1625571 Wang, R., Kim, S. J., & Malthouse, E. (2016). Branded apps and mobile platforms as new tools for advertising. In R. E. Brown, V. K. Jones, M. Wang (Eds.), The new advertising: Branding, content and consumer relationships in the data-driven social media era (Vol. 2, pp. 123–156). Praeger. https://doi.org/10.13140/RG.2.1.3744.3042 Wilensky, U. (1999). NetLogo home page. https://ccl.northwestern.edu/netlogo/ Zareie, A., Sheikhahmadi, A., & Jalili, M. (2019). Identification of influential users in social networks based on users’ interest. Information Sciences, 493, 217–231. https://doi.org/10. 1016/j.ins.2019.04.033 |
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García-Dávalos, AlexanderGarcía-Duque, JorgeTaylor & Francis Group2024-04-12T20:07:40Z2024-04-12T20:07:40Z2023García-Dávalos, A.; García-Duque, J. (2023). A Simplified Mobile Advertising Model to Study Advertising Spreading through Personal Social Networks and Branded Apps. Journal of Promotion Management. 29(8). p.p. 1104-1137. https://doi.org/10.1080/10496491.2023.21892041049-6491https://hdl.handle.net/10614/15530https://doi.org/10.1080/10496491.2023.2189204Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/Research on mobile advertising has evolved considerably over the last few years. However, several under-researched topics remain, including mobile advertising spreading by leveraging users’ personal social networks. The present research addresses this gap by proposing a model for studying advertising spreading through personal networks using a viral approach and branded apps. The proposed model simulation suggests that the influence of spreader users’ percentage in ad spreading was significant, but increasing it beyond 20% did not impact the spreading growth. Additionally, including user privacy perception in the model positively affected spreading by increasing advertising propagation risk34 páginasapplication/pdfengTaylor & Francis GroupReino UnidoDerechos reservados - Taylor y Francis Group, 2023https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2A simplified mobile advertising model to study advertising spreading through personal social networks and branded appsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a8511378110429Journal of Promotion ManagementAdam, D. C., Wu, P., Wong, J. Y., Lau, E. H. Y., Tsang, T. K., Cauchemez, S., Leung, G. M., & Cowling, B. J. (2020). Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nature Medicine, 26(11), 1714–1719. https://doi.org/10.1038/ s41591-020-1092-0Akpojivi, U., & Bevan-Dye, A. (2015). Mobile advertisements and information privacy perception amongst South African Generation Y students. Telematics and Informatics, 32(1), 1–10. https://doi.org/10.1016/j.tele.2014.08.001Alsuwaidan, L., & Ykhlef, M. (2016). Toward information diffusion model for viral marketing in business. International Journal of Advanced Computer Science and Applications, 7(2), 637–646. https://doi.org/10.14569/IJACSA.2016.070280AMA. (2021, October 11). Statement of ethics. https://myama.my.site.com/s/article/AMAStatement- of-Ethics Arnaboldi, V., Conti, M., Passarella, A., & Dunbar, R. I. M. (2017). Online social networks and information diffusion: The role of ego networks. Online Social Networks and Media, 1, 44–55. https://doi.org/10.1016/j.osnem.2017.04.001Atdag, S., & Bingol, H. O. (2021). Computational models for commercial advertisements in social networks. Physica A: Statistical Mechanics and Its Applications, 572, 125916. https://doi.org/10.1016/j.physa.2021.125916Baek, T. H., & Morimoto, M. (2012). Stay away from me. Journal of Advertising, 41(1), 59– 76. https://doi.org/10.2753/JOA0091-3367410105Bakhshandeh, R., Samadi, M., Azimifar, Z., Schaeffer, J. (2011). Degrees of separation in social networks. Proceedings of the International Symposium on Combinatorial Search, 2(1), 18–23. https://doi.org/10.1609/socs.v2i1.18200Banerjee, S., Jenamani, M., & Pratihar, D. K. (2020). A survey on influence maximization in a social network. Knowledge and Information Systems, 62(9), 3417–3455. https://doi. org/10.1007/s10115-020-01461-4Barutcu, S., Yaldir, A., & Hasiloglu, S. B. (2017). From mass to personalized mobile marketing strategies: The new dimensions through expert systems. European Scientific Journal, 13(10), 400–409. https://doi.org/10.19044/esj.2017.v13n10p%pBellman, S., Potter, R. F., Treleaven-Hassard, S., Robinson, J. A., & Varan, D. (2011). The effectiveness of branded mobile phone apps. Journal of Interactive Marketing, 25(4), 191– 200. https://doi.org/10.1016/j.intmar.2011.06.001Bhattacharya, S., Gaurav, K., & Ghosh, S. (2019). Viral marketing on social networks: An epidemiological perspective. Physica A: Statistical Mechanics and Its Applications, 525, 478–490. https://doi.org/10.1016/j.physa.2019.03.008Boerman, S. C., & Smit, E. G. (2023). Advertising and privacy: An overview of past research and a research agenda. International Journal of Advertising, 42(1), 60–68. https://doi.org/10.1080/02650487.2022.2122251Briskman, J. (2019, April 29). Top sneaker apps in the U.S. from 2015 to 2018. Sensor Tower Blog. https://sensortower.com/blog/top-sneaker-apps-us-2015-2018Carroll, C., Bhattacharjee, S., Chen, Y., Dubey, P., Fan, J., Gajardo, A., Zhou, X., M€uller, H.-G., & Wang, J. L. (2020). Time dynamics of COVID-19. Scientific Reports, 10(1), 1– 14. https://doi.org/10.1038/s41598-020-77709-4CDC. (2021, December 20). Principles of epidemiology j Lesson 3—Section 2. https://www. cdc.gov/csels/dsepd/ss1978/lesson3/section2.htmlChen, Z. (2019). An agent-based model for information diffusion over online social networks. Papers in Applied Geography, 5(1–2), 77–97. https://doi.org/10.1080/23754931.2019.1619193Chu, S.-C., & Kim, J. (2018). The current state of knowledge on electronic word-of-mouth in advertising research. International Journal of Advertising, 37(1), 1–13. https://doi.org/ 10.1080/02650487.2017.1407061de Souza, R. C., Figueiredo, D. R., de, A., Rocha, A. A., & Ziviani, A. (2020). Efficient network seeding under variable node cost and limited budget for social networks. Information Sciences, 514, 369–384. https://doi.org/10.1016/j.ins.2019.11.029Degirmenci, K. (2020). Mobile users’ information privacy concerns and the role of app permission requests. International Journal of Information Management, 50, 261–272. https:// doi.org/10.1016/j.ijinfomgt.2019.05.010Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021). Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. Journal of Business Research, 135, 758–773. https://doi.org/10.1016/j.jbusres. 2021.07.015Eckler, P., & Bolls, P. (2011). Spreading the virus: Emotional tone of viral advertising and its effect on forwarding intentions and attitudes. Journal of Interactive Advertising, 11(2), 1–11. https://doi.org/10.1080/15252019.2011.10722180Endo, A., Abbott, S., Kucharski, A. J., & Funk, S. (2020). Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China (5:67). Wellcome Open Research, 5, 67. https://doi.org/10.12688/wellcomeopenres.15842.3Furini, M., Mirri, S., Montangero, M., & Prandi, C. (2020). Privacy perception when using smartphone applications. Mobile Networks and Applications, 25(3), 1055–1061. https:// doi.org/10.1007/s11036-020-01529-zGarcia-Davalos, A., & Garcia-Duque, J. (2020). User profile modelling based on mobile phone sensing and call logs. In A. Rocha, C. Ferr as, C. E. Montenegro Marin, & V. H. Medina Garc ıa (Eds.), Information technology and systems (pp. 243–254). Springer International Publishing. https://doi.org/10.1007/978-3-030-40690-5_24Ghosh, S., Bhattacharya, S., Gaurav, K., & Singh, Y. N. (2018). Going viral: The epidemiological strategy of referral marketing. (arXiv:180803780). arXiv. https://doi.org/10.48550/ arXiv.1808.03780G€ursakal, N., Batmaz, B., & Aktuna, G. (2020). Drawing transmission graphs for COVID- 19 in the perspective of network science. Epidemiology and Infection, 148, E269. https:// doi.org/10.1017/S0950268820002654Hayes, J. L., King, K. W., & Ramirez, A. (2016). Brands, friends, & viral advertising: A social exchange perspective on the ad referral processes. Journal of Interactive Marketing, 36, 31–45. https://doi.org/10.1016/j.intmar.2016.04.001Himelboim, I., & Golan, G. J. (2019). A social networks approach to viral advertising: The role of primary, contextual, and low influencers. Social MediaþSociety, 5(3), 2056305119847516. https://doi.org/10.1177/2056305119847516Jebarajakirthy, C., Maseeh, H. I., Morshed, Z., Shankar, A., Arli, D., & Pentecost, R. (2021). Mobile advertising: A systematic literature review and future research agenda. International Journal of Consumer Studies, 45(6), 1258–1291. https://doi.org/10.1111/ijcs. 12728Jing, Y., Peiyu, L., Xiaobing, T., & Wenfeng, L. (2018). Improved SIR advertising spreading model and its effectiveness in social network. Procedia Computer Science, 129, 215–218. https://doi.org/10.1016/j.procs.2018.03.044Jingyan, L., & Liwen, H. (2018). Mobile advertising optimization strategy based on SICAS model in China. International Journal of Business and Management, 13(11), 169–175. https://doi.org/10.5539/ijbm.v13n11p169Kelly, L., Kerr, G., Drennan, J., & Fazal-E-Hasan, S. M. (2021). Feel, think, avoid: Testing a new model of advertising avoidance. Journal of Marketing Communications, 27(4), 343– 364. https://doi.org/10.1080/13527266.2019.1666902Kempe, D., Kleinberg, J., & Tardos, E. (2015). Maximizing the spread of influence through a social network. Theory of Computing, 11(1), 105–147. https://doi.org/10.4086/toc.2015. v011a004Kim, M., & Fernandez, R. M. (2017). Strength matters: Tie strength as a causal driver of networks’ information benefits. Social Science Research, 65, 268–281. https://doi.org/10. 1016/j.ssresearch.2016.11.003Kweon, H., Zhang, D., & Zhou, L. (2012). User privacy in mobile advertising. In AMCIS 2012 Proceedings. https://aisel.aisnet.org/amcis2012/proceedings/HCIStudies/1Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM Transactions on the Web, 1(1), 5-es. https://doi.org/10.1145/1232722.1232727Leskovec, J., & Horvitz, E. (2008). Planetary-scale views on a large instant-messaging network. In Proceedings of the 17th International Conference on World Wide Web, pp. 915– 924. https://doi.org/10.1145/1367497.1367620Li, Y., Fan, J., Wang, Y., & Tan, K.-L. (2018). Influence maximization on social graphs: A survey. IEEE Transactions on Knowledge and Data Engineering, 30(10), 1852–1872. https://doi.org/10.1109/TKDE.2018.2807843Liu, F., Kanso, A., Zhang, Y., & Olaru, D. (2019). Culture, perceived value, and advertising acceptance: A cross-cultural study on mobile advertising. Journal of Promotion Management, 25(7), 1028–1058. https://doi.org/10.1080/10496491.2019.1612495Li, M., Wang, X., Gao, K., & Zhang, S. (2017). A survey on information diffusion in online social networks: Models and methods. Information, 8(4), 118. https://doi.org/10.3390/ info8040118Maseeh, H. I., Jebarajakirthy, C., Pentecost, R., Ashaduzzaman, M., Arli, D., & Weaven, S. (2021). A meta-analytic review of mobile advertising research. Journal of Business Research, 136, 33–51. https://doi.org/10.1016/j.jbusres.2021.06.022Mittal, S., & Kumar, V. (2020). A framework for ethical mobile marketing. International Journal of Technoethics, 11, 28–42. https://doi.org/10.4018/IJT.2020010103Moldovan, S., Steinhart, Y., & Lehmann, D. R. (2019). Propagators, creativity, and informativeness: What helps ads go viral. Journal of Interactive Marketing, 47(1), 102–114. https://doi.org/10.1016/j.intmar.2019.02.004Morimoto, 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. 1080/02650487.2020.1796322Nielsen. (2015, September 28). Recommendations from friends remain most credible form of advertising among consumers; Branded websites are the second highest rated form. https:// www.nielsen.com/eu/en/press-releases/2015/recommendations-from-friends-remain-mostcredible- form-of-advertisingPalka, W., Pousttchi, K., & Wiedemann, D. G. (2009). Mobile word-of-mouth – A grounded theory of mobile viral marketing. Journal of Information Technology, 24(2), 172–185. https://doi.org/10.1057/jit.2008.37Razaque, A., Rizvi, S., Khan, M. J., Almiani, M., & Rahayfeh, A. A. (2022). State-of-art review of information diffusion models and their impact on social network vulnerabilities. Journal of King Saud University - Computer and Information Sciences, 34(1), 1275– 1294. https://doi.org/10.1016/j.jksuci.2019.08.008Rodrigues, H. S., & Fonseca, M. J. (2016). Can information be spread as a virus? Viral marketing as epidemiological model. Mathematical Methods in the Applied Sciences, 39(16), 4780–4786. https://doi.org/10.1002/mma.3783Salehi, M., Sharma, R., Marzolla, M., Magnani, M., Siyari, P., & Montesi, D. (2015). Spreading processes in multilayer networks. IEEE Transactions on Network Science and Engineering, 2(2), 65–83. https://doi.org/10.1109/TNSE.2015.2425961Sarin, C., & Pal, B. (2014). Word of mouth marketing: Consumers participation. The International Journal of Business & Management, 2(1), 74–79. http://www.internationaljournalcorner. com/index.php/theijbm/article/view/128400Segev, S., & Fernandes, J. (2022). The anatomy of viral advertising: A content analysis of viral advertising from the elaboration likelihood model perspective. Journal of Promotion Management, 29(1), 1–30. https://doi.org/10.1080/10496491.2022.2108189Sen, S., & Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the Web. Journal of Interactive Marketing, 21(4), 76–94. https://doi. org/10.1002/dir.20090Shaikh, B. (2014). Understanding word of mouth communication: A theoretical review of literature. The International Journal of Business & Management, 2(4), 58–64. http:// www.internationaljournalcorner.com/index.php/theijbm/article/view/127642Sigurdsson, V., Menon, R. G. V., Hallgr ımsson, A. G., Larsen, N. M., & Fagerstrøm, A. (2018). Factors affecting attitudes and behavioral intentions toward in-app mobile advertisements. Journal of Promotion Management, 24(5), 694–714. https://doi.org/10.1080/ 10496491.2018.1405523Silva, E., Pereira, F. H., & Schimit, P. H. T. (2020). Information spreading in a population modeled by continuous asynchronous probabilistic cellular automata. Computer Communications, 154, 288–297. https://doi.org/10.1016/j.comcom.2020.02.074Statista. (2021). Mobile ad blocking penetration U.S. 2020. https://www.statista.com/statistics/ 554304/interest-mobile-ad-blocking/Statista. (2022). Mobile ad spend worldwide 2024. https://www.statista.com/statistics/303817/ mobile-internet-advertising-revenue-worldwide/Stocchi, L., Michaelidou, N., & Micevski, M. (2019). Drivers and outcomes of branded mobile app usage intention. Journal of Product & Brand Management, 28(1), 28–49. https://doi.org/10.1108/JPBM-02-2017-1436Stonedahl, F., & Wilensky, U. (2008). NetLogo models library: Virus on a network. http:// ccl.northwestern.edu/netlogo/models/VirusonaNetworkTang, A. K. Y. (2019). A systematic literature review and analysis on mobile apps in mcommerce: Implications for future research. Electronic Commerce Research and Applications, 37, 100885. https://doi.org/10.1016/j.elerap.2019.100885Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 51(5), 546–562. https://doi.org/10.1509/jmr.10.0355Ullah, I., Boreli, R., Kanhere, S. S., Chawla, S., Ahanger, T. A., & Tariq, U. (2020). Protecting private attributes in app based mobile user profiling. IEEE Access, 8, 143818– 143836. https://doi.org/10.1109/ACCESS.2020.3014424Ullah, I., Boreli, R., & Kanhere, S. S. (2021). Privacy in targeted advertising: A survey. arXiv preprint. http://arxiv.org/abs/2009.06861Ullah, I., Kanhere, S. S., & Boreli, R. (2020). Privacy-preserving targeted mobile advertising: A blockchain-based framework for mobile ads. ArXiv:2008.10479 [Cs]. http://arxiv.org/ abs/2008.10479Um, N.-H. (2019). Predictors of mobile advertising avoidance: What makes people avoid mobile advertising? International Journal of Contents, 15(4), 44–49. https://doi.org/10. 5392/IJoC.2019.15.4.044van Noort, G., & van Reijmersdal, E. A. (2019). Branded apps: Explaining effects of brands’ mobile phone applications on brand responses. Journal of Interactive Marketing, 45, 16– 26. https://doi.org/10.1016/j.intmar.2018.05.003Wang, T., Duong, T. D., & Chen, C. C. (2016). Intention to disclose personal information via mobile applications: A privacy calculus perspective. International Journal of Information Management, 36(4), 531–542. https://doi.org/10.1016/j.ijinfomgt.2016.03.003Wang, Y., Genc, E., & Peng, G. (2020). Aiming the mobile targets in a cross-cultural context: Effects of trust, privacy concerns, and attitude. International Journal of Human– Computer Interaction, 36(3), 227–238. https://doi.org/10.1080/10447318.2019.1625571Wang, R., Kim, S. J., & Malthouse, E. (2016). Branded apps and mobile platforms as new tools for advertising. In R. E. Brown, V. K. Jones, M. Wang (Eds.), The new advertising: Branding, content and consumer relationships in the data-driven social media era (Vol. 2, pp. 123–156). Praeger. https://doi.org/10.13140/RG.2.1.3744.3042Wilensky, U. (1999). NetLogo home page. https://ccl.northwestern.edu/netlogo/Zareie, A., Sheikhahmadi, A., & Jalili, M. (2019). Identification of influential users in social networks based on users’ interest. 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