Factors that determine advertising evasion in social networks
The present work is framed within the study of advertising evasion online and particularly in social networks. Social networks are a growing phenomenon, where users spend most of their time online and where companies are moving part of their advertising investment, as they are considered an ideal pl...
- Autores:
-
Silva, Jesús
Pinillos-Patiño, Yisel
Sukier, Harold
Vargas, Jesús
Corrales, Patricio
Pineda Lezama, Omar Bonerge
Quintero, Benjamín
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7703
- Acceso en línea:
- https://hdl.handle.net/11323/7703
https://doi.org/10.1007/978-981-15-7234-0_81
https://repositorio.cuc.edu.co/
- Palabra clave:
- Perceived control
Intrusion
Reactance
Advertising evasion
Social networks
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Factors that determine advertising evasion in social networks |
| title |
Factors that determine advertising evasion in social networks |
| spellingShingle |
Factors that determine advertising evasion in social networks Perceived control Intrusion Reactance Advertising evasion Social networks |
| title_short |
Factors that determine advertising evasion in social networks |
| title_full |
Factors that determine advertising evasion in social networks |
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Factors that determine advertising evasion in social networks |
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Factors that determine advertising evasion in social networks |
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Factors that determine advertising evasion in social networks |
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Silva, Jesús Pinillos-Patiño, Yisel Sukier, Harold Vargas, Jesús Corrales, Patricio Pineda Lezama, Omar Bonerge Quintero, Benjamín |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Pinillos-Patiño, Yisel Sukier, Harold Vargas, Jesús Corrales, Patricio Pineda Lezama, Omar Bonerge Quintero, Benjamín |
| dc.subject.spa.fl_str_mv |
Perceived control Intrusion Reactance Advertising evasion Social networks |
| topic |
Perceived control Intrusion Reactance Advertising evasion Social networks |
| description |
The present work is framed within the study of advertising evasion online and particularly in social networks. Social networks are a growing phenomenon, where users spend most of their time online and where companies are moving part of their advertising investment, as they are considered an ideal place for commercial campaigns. In order to deepen in the variables that precede advertising evasion in social networks, a relationship model was developed based on the theoretical framework of advertising evasion on the Internet, which was contrasted at an empirical level through a panel of users. For this purpose, a structural equation model was designed, which highlighted the relationships between the main antecedent variables of evasion, such as perceived control, advertising intrusion, and psychological reaction. |
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2021 |
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2021-01-15T21:46:25Z |
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2021 |
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Artículo de revista |
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Corporación Universidad de la Costa |
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eng |
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1. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Magazine 17(3):37–54 2. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers 3. WEKA 3 (2016) Data mining software in Java homepage. 4. Singh Y, Chanuhan A (2009) Neural networks in data mining. J Theor Appl Inf Technol 5(1):37–42 5. Orallo J, Ramírez M, Ferri C (2008) Introducción a la Minería de Datos. Pearson Education 6. Aladag C, Hocaoglu G (2007) A tabu search algorithm to solve a course timetabling problem. Hacettepe J Math Stat 53–64 7. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards Memetic algorithms. Caltech Concurrent Computation Program (report 826) (1989) 8. Frausto-Solís J, Alonso-Pecina F, Mora-Vargas J (2008) An efficient simulated annealing algorithm for feasible solutions of course timetabling. Springer, pp 675–685 9. Joudaki M, Imani M, Mazhari N (2010) Using improved Memetic algorithm and local search to solve University Course Timetabling Problem (UCTTP). Islamic Azad University, Doroud, Iran 10. Coopers PWH (2014) IAB internet advertising revenue report. 11. Tuzhilin A (2006) The lane’s gifts v. Google report. Official Google Blog: Findings on invalid clicks, pp 1–47 12. Ponce H, Ponce P, Molina A (2014) Artificial organic networks: artificial intelligence based on carbon networks. In: Studies in computational intelligence, vol 521. Springer 13. Ponce H, Ponce P, Molina A (2013) A new training algorithm for artificial hydrocarbon networks using an energy model of covalent bonds. In: 7th IFAC conference on manufacturing modelling, management, and control, vol 7, issue 1, pp 602–608 14. Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham 15. Moe WW (2013) Targeting display advertising. Advanced database marketing: Innovative methodologies & applications for managing customer relationships. Gower Publishing, Londres 16. Granitto PM, Furlanello C, Biasioli F, Gasperi F (2006) Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometr Intell Lab Syst 83(2):83–90 17. Kuhn W, Wing J, Weston S, Williams A, Keefer C et al (2012) Caret: classification and regression training. R package, v515 18. Miller B, Pearce P, Grier C, Kreibich C, Paxson V (2011) What’s clicking what? Techniques and innovations of today’s clickbots. In: Detection of intrusions and malware, and vulnerability assessment. Springer, pp 164–183 19. Kamatkar SJ, Tayade A, Viloria A, Hernández-Chacín A (2018) Application of classification technique of data mining for employee management system. In: International conference on data mining and big data. Springer, Cham, pp 434–444 20. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data. Springer, Cham, pp 3–11 21. Ellison NB, Steinfield C, Lampe C (2007) The benefits of Facebook “Friends:” Social capital and college students’ use of online social network sites. J Comput Med Commun 12(4):1143–1168 22. Silva J, Hernández-Fernández L, Cuadrado ET, Mercado-Caruso N, Espinosa CR, Ortega FA, Hernández H, Delgado GJ (2019) Factors affecting the big data adoption as a marketing tool in SMEs. In: International conference on data mining and big data. Springer, Singapore, pp 34–43 |
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Silva, JesúsPinillos-Patiño, YiselSukier, HaroldVargas, JesúsCorrales, PatricioPineda Lezama, Omar BonergeQuintero, Benjamín2021-01-15T21:46:25Z2021-01-15T21:46:25Z2021https://hdl.handle.net/11323/7703https://doi.org/10.1007/978-981-15-7234-0_81Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The present work is framed within the study of advertising evasion online and particularly in social networks. Social networks are a growing phenomenon, where users spend most of their time online and where companies are moving part of their advertising investment, as they are considered an ideal place for commercial campaigns. In order to deepen in the variables that precede advertising evasion in social networks, a relationship model was developed based on the theoretical framework of advertising evasion on the Internet, which was contrasted at an empirical level through a panel of users. For this purpose, a structural equation model was designed, which highlighted the relationships between the main antecedent variables of evasion, such as perceived control, advertising intrusion, and psychological reaction.Silva, JesúsPinillos-Patiño, YiselSukier, HaroldVargas, JesúsCorrales, PatricioPineda Lezama, Omar BonergeQuintero, Benjamínapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_81Perceived controlIntrusionReactanceAdvertising evasionSocial networksFactors that determine advertising evasion in social networksArtículo 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/acceptedVersion1. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Magazine 17(3):37–542. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers3. WEKA 3 (2016) Data mining software in Java homepage.4. Singh Y, Chanuhan A (2009) Neural networks in data mining. J Theor Appl Inf Technol 5(1):37–425. Orallo J, Ramírez M, Ferri C (2008) Introducción a la Minería de Datos. Pearson Education6. Aladag C, Hocaoglu G (2007) A tabu search algorithm to solve a course timetabling problem. Hacettepe J Math Stat 53–647. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards Memetic algorithms. Caltech Concurrent Computation Program (report 826) (1989)8. Frausto-Solís J, Alonso-Pecina F, Mora-Vargas J (2008) An efficient simulated annealing algorithm for feasible solutions of course timetabling. Springer, pp 675–6859. Joudaki M, Imani M, Mazhari N (2010) Using improved Memetic algorithm and local search to solve University Course Timetabling Problem (UCTTP). Islamic Azad University, Doroud, Iran10. Coopers PWH (2014) IAB internet advertising revenue report.11. Tuzhilin A (2006) The lane’s gifts v. Google report. Official Google Blog: Findings on invalid clicks, pp 1–4712. Ponce H, Ponce P, Molina A (2014) Artificial organic networks: artificial intelligence based on carbon networks. In: Studies in computational intelligence, vol 521. Springer13. Ponce H, Ponce P, Molina A (2013) A new training algorithm for artificial hydrocarbon networks using an energy model of covalent bonds. In: 7th IFAC conference on manufacturing modelling, management, and control, vol 7, issue 1, pp 602–60814. Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham15. Moe WW (2013) Targeting display advertising. Advanced database marketing: Innovative methodologies & applications for managing customer relationships. Gower Publishing, Londres16. Granitto PM, Furlanello C, Biasioli F, Gasperi F (2006) Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometr Intell Lab Syst 83(2):83–9017. Kuhn W, Wing J, Weston S, Williams A, Keefer C et al (2012) Caret: classification and regression training. R package, v51518. Miller B, Pearce P, Grier C, Kreibich C, Paxson V (2011) What’s clicking what? Techniques and innovations of today’s clickbots. In: Detection of intrusions and malware, and vulnerability assessment. Springer, pp 164–18319. Kamatkar SJ, Tayade A, Viloria A, Hernández-Chacín A (2018) Application of classification technique of data mining for employee management system. In: International conference on data mining and big data. Springer, Cham, pp 434–44420. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data. Springer, Cham, pp 3–1121. Ellison NB, Steinfield C, Lampe C (2007) The benefits of Facebook “Friends:” Social capital and college students’ use of online social network sites. J Comput Med Commun 12(4):1143–116822. Silva J, Hernández-Fernández L, Cuadrado ET, Mercado-Caruso N, Espinosa CR, Ortega FA, Hernández H, Delgado GJ (2019) Factors affecting the big data adoption as a marketing tool in SMEs. In: International conference on data mining and big data. 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