Academic literature on the topic 'Types of online advertising'
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Journal articles on the topic "Types of online advertising"
Anusha. "EFFECTIVENESS OF ONLINE ADVERTISING." International Journal of Research -GRANTHAALAYAH 4, no. 3SE (March 31, 2016): 14–21. http://dx.doi.org/10.29121/granthaalayah.v4.i3se.2016.2772.
Full textKorgaonkar, Pradeep K., Ronnie Silverblatt, and Enrique P. Becerra. "The Hispanic View of E-mail, Popup, and Banner Advertising." International Journal of E-Business Research 6, no. 2 (April 2010): 45–58. http://dx.doi.org/10.4018/jebr.2010040103.
Full textEka Santi, Devita. "PERAN ONLINE ADVERTISING PADA PEMASARAN AXIS." SOURCE : Jurnal Ilmu Komunikasi 6, no. 1 (April 28, 2020): 58. http://dx.doi.org/10.35308/source.v6i1.1768.
Full textWatts, Alison. "Fairness and Efficiency in Online Advertising Mechanisms." Games 12, no. 2 (April 15, 2021): 36. http://dx.doi.org/10.3390/g12020036.
Full textErmakova, Irina Viktorovna. "Protection of consumer rights from unfair online advertising: certain theoretical and practical aspects." Юридические исследования, no. 7 (July 2021): 29–47. http://dx.doi.org/10.25136/2409-7136.2021.7.35978.
Full textAnikina, Tatiana Vyacheslavovna. "Syntactic peculiarities of English-language and Russian-language online advertising." Филология: научные исследования, no. 1 (January 2021): 23–40. http://dx.doi.org/10.7256/2454-0749.2021.1.34886.
Full textFranz, Michael M., Erika Franklin Fowler, Travis Ridout, and Meredith Yiran Wang. "The Issue Focus of Online and Television Advertising in the 2016 Presidential Campaign." American Politics Research 48, no. 1 (September 16, 2019): 175–96. http://dx.doi.org/10.1177/1532673x19875722.
Full textKareem, Rafeef, and Wesam Bhaya. "Fake Profiles Types of Online Social Networks: A Survey." International Journal of Engineering & Technology 7, no. 4.19 (November 27, 2018): 919. http://dx.doi.org/10.14419/ijet.v7i4.19.28071.
Full textBreuer, Ralph, Malte Brettel, and Andreas Engelen. "Incorporating long-term effects in determining the effectiveness of different types of online advertising." Marketing Letters 22, no. 4 (March 16, 2011): 327–40. http://dx.doi.org/10.1007/s11002-011-9136-3.
Full textGong, Shiyang, Wanqin Wang, and Qian Li. "Marketing communication in the digital age: online ads, online WOM and mobile game adoptions." Nankai Business Review International 10, no. 3 (August 5, 2019): 382–407. http://dx.doi.org/10.1108/nbri-12-2018-0073.
Full textDissertations / Theses on the topic "Types of online advertising"
Waechter, Susanne Franziska. "Contextual advertising in online communication: An investigation of relationships between multiple content types on a webpage." AUT University, 2010. http://hdl.handle.net/10292/912.
Full textAl-Rubaiee, Seror, and Simon Lind. "Influencer advertising, sponsored, and non-sponsored: A study on how message types affect consumer purchase intention." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19697.
Full textUhrová, Pavlína. "Analýza a návrh elektronického obchodu firmy." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2013. http://www.nusl.cz/ntk/nusl-224194.
Full textPettersson, Jimmie. "Contextual Advertising Online." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15878.
Full textThe internet advertising market is growing much faster than any other advertising vertical. The technology for serving advertising online goes more and more towards automated processes that analyze the page content and the user’s preferences and then matches the ads with these parameters.
The task at hand was to research and find methods that could be suitable for matching web documents to ads automatically, build a prototype system, make an evaluation and suggest areas for further development. The goals of the system was high throughput, accurate ad matching and fast response times. A requirement on the system was that human input could only be done when adding ads into the system for the system to be scalable.
The prototype system is based on the vector space model and a td-idf weighting scheme. The cosines coefficient was used in the system to quantify the similarity between a web document and an ad.
A technique called stemming was also implemented in the system together with a clustering solution that aided the ad matching in cases where few matches could be done on the keywords attached to the ads. The system was built with a threaded structure to improve throughput and scalability.
The tests results show that you accurately can match ads to a website’s content using the vector space model and the cosines-coefficient. The tests also show that the stemming has a positive effect on the ad matching accuracy.
Liu, Handan, and Jin Ma. "Advertising management influence effctiveness of online advertising : A study of white-collar workers in online advertising context." Thesis, Halmstad University, School of Business and Engineering (SET), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-4868.
Full textOnline advertising which is a new form of communication provides interactivity between advertisers and consumers.
This dissertation is focusing on how advertising management influence the effectiveness of online advertising.The theoretical framework of this study mainly contains the advertising management and effectiveness of effects model.These two models are combined together in an analytical model where a connection between the two theories is explained that will be used as a foundation in gathering and analyzing the empirical findings. The quatitative research strategy is appliedd in this dissertation. Based on the questionnaies of white-collar workers, the empirical data was collected.
The conclusion which can be drawn from this study is that there is clear evidence that good management will improve cognitive,affective and conative degree of consumer behavior.
Estrada, Jiménez José Antonio. "Privacy in online advertising platforms." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669772.
Full textLa publicitat en línia té un paper important a Internet que permet finançar habitualment l'operació de llocs web que ofereixen contingut lliure als usuaris. A més, la personalització dels anuncis ha tornat la publicitat en línia un servei valuós per als usuaris. Si aconseguirem que hi hagi molts compradors siguin més que possibles, es promourà un negoci milionari. El model d'anuncis vigents es basa en una infraestructura completa que lliura els anuncis personalitzats. Pera això, es pot recopilar una gran quantitat de dades d'ús, agregar, processar i vendre molt ràpidament. Malauradament, aquestes pràctiques generen riscos de privadesa. Donada la complexitat de l'operació de les plataformes d'anuncis, els riscos de privacitat es poden estudiar des de diverses perspectives. Naturalment, els esforços per desenvolupar aquests problemes de privacitat es concentren en una entitat, tecnologia, comportament o context específic. Però aquest enfocament subestima els beneficis d'una perspectiva més àmplia d'un problema integral. Molts mecanismes de protecció han estat proposats des de la indústria i l’àmbit acadèmic. Els més populars apliquen estratègies radicals que obstrueixen la distribució d'anuncis, afectant seriosament l’ecosistema d'anuncis. També es pot modificar significativament l’ecosistema, el que no és factible per la seva conflictivitat. Així, amb la finalitat de fomentar l'adopció de protecció de privacitat, és fonamental plantejar solucions orientades a equilibrar les necessitats de privacitat amb el model de negocis de la web. Inicialment, la tesi ofereix una visió amplia dels riscos de privacitat i els mecanismes de protecció a ecosistema d'anuncis en línia. Això es pot aconseguir basant-se en una revisió de la infraestructura i tecnologies subjacents en aquest context. Analitza sistemàticament les amenaces i potencies atacants. A continuació es revisa exhaustivament els mecanismes de privacitat més rellevants, i es classifica i es compara segons les garanties de privacitat que s'ofereixen i el seu possible impacte a la web. Seguidament, s'estudia els riscos de privadesa derivats de les ofertes en temps real, una tecnologia clau del sistema d'anuncis en línia modern. Experimentalment, s'inverteixen els riscos del procés de distribució de dades d'ús, part del sistema basat en licitacions de la publicitat en línia. Es proposa un sistema que regula la distribució de dades d'ús a tercers, depenent del seu comportament previ. Això consisteix en reduir el nombre d’agències anunciants que rebin dades d'ús. Per mitigar l’impacte sobre els ingressos del sistema d'anuncis, aquesta reducció és malaltia i l'objectiu de maximitzar els declaracions ingressades. Experimentalment, es troba que el sistema proposat corregir els comportaments maliciosos, millorant la privacitat dels usuaris. Finalment, s'analitza l'impacte del rastre i la publicitat en línia des de la perspectiva iberoamericana. Estudiem el rastreig de tercers i allò relacionat amb els anuncis que se generen en llocs web locals en aquesta regió heterogènia. Trobem que la ubicació de l'usuari en aquest context afecta la privacitat de l'usuari ja que aquest rastreig varia de país a país, tot i que el nombre total d'entitats darrere d'aquest transit sembla estable. El tipus de contingut afecta també el nivell de rastreig: llocs web de noticies o de compres generen més transit cap a tercers i aquesta intensitat s'exacerba en els llocs més populars.
Chen, B. "Financial methods for online advertising." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1461143/.
Full textGreen, Katherine Elizabeth. "What makes different types of advertising effective?" Thesis, University of Newcastle Upon Tyne, 2008. http://hdl.handle.net/10443/160.
Full textCox, Shirley A. "Online social network member attitude toward online advertising formats /." Online version of thesis, 2010. http://hdl.handle.net/1850/11588.
Full textTran, Minh-Dung. "Privacy Challenges in Online Targeted Advertising." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM053/document.
Full textIn modern online advertising, advertisers tend to track Internet users' activities and use these tracking data to personalize ads. Even though this practice - known as extit{targeted advertising} - brings economic benefits to advertising companies, it raises serious concerns about potential abuses of users' sensitive data. While such privacy violations, if performed by trackers, are subject to be regulated by laws and audited by privacy watchdogs, the consequences of data leakage from these trackers to other entities are much more difficult to detect and control. Protecting user privacy is not easy since preventing tracking undermines the benefits of targeted advertising and consequently impedes the growth of free content and services on the Internet, which are mainly fostered by advertising revenue. While short-term measures, such as detecting and fixing privacy leakages in current systems, are necessary, there needs to be a long-term approach, such as privacy-by-design ad model, to protect user privacy by prevention rather than cure. In the first part of this thesis, we study several vulnerabilities in current advertising systems that leak user data from advertising companies to external entities. First, since targeted ads are personalized to each user, we present an attack exploiting these ads on the fly to infer user private information that have been used to select ads. Second, we investigate common ad exchange protocols, which allow companies to cooperate in serving ads to users, and show that advertising companies are leaking user private information, such as web browsing history, to multiple parties participating in the protocols. These web browsing histories are given to these entities at surprisingly low prices, reflecting the fact that user privacy is extremely underestimated by the advertising industry.In the second part of the thesis, we propose a privacy-by-design targeted advertising model which allows personalizing ads to users without the necessity of tracking. This model is specifically aimed for the two newly emerging ad technologies - retargeting advertising and ad exchange. We show that this model provides strong protection for user privacy while still ensuring ad targeting performance and being practically deployable
Books on the topic "Types of online advertising"
Advertising: Types, trends, and controversies. Hauppauge, N.Y: Nova Science Publisher's, 2011.
Find full textEberts, Marjorie. Careers for cybersurfers & other online types. Lincolnwood, Ill: VGM Career Horizons, 1998.
Find full textZhu, Xingquan, Haicheng Tao, Zhiang Wu, Jie Cao, Kristopher Kalish, and Jeremy Kayne. Fraud Prevention in Online Digital Advertising. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56793-8.
Full textHua, Xian-Sheng. Online multimedia advertising: Techniques and technologies. Hershey, PA: Information Science Reference, 2011.
Find full textMiller, Michael. Online marketing heroes: Interviews with 25 successful online marketing gurus. Indianapolis, IN: Wiley Technology Pub, 2009.
Find full textThe mood of information: Online behavioural advertising. New York, NY: Continuum International Pub. Group, 2011.
Find full textMeyerson, Mitch. Mastering online marketing. [Irvine, CA]: Entrepreneur Press, 2008.
Find full textLevinson, Jay Conrad. Guerrilla marketing online weapons: 100 low-cost, high-impact weapons for online profits and prosperity. Boston: Houghton Mifflin Co., 1996.
Find full textBook chapters on the topic "Types of online advertising"
Jiang, Xianglan, Yingjin Lu, and Wen-bo Yang. "The Online Advertising Types of Choices Based on DEA Method." In Lecture Notes in Economics and Mathematical Systems, 157–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19695-9_13.
Full textSt-Onge, Anik, Sylvain Senecal, Marc Fredette, and Jacques Nantel. "Is Targeting Online Information Diffusers Based on Their Personality Traits and Influencer Types Misleading?" In Advances in Advertising Research (Vol. VII), 79–86. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15220-8_7.
Full textHe, Xiang, Li Li, Hua Zhang, and Xingzhen Zhu. "Social Media or Website? Research on Online Advertising Type Based on Evolutionary Game." In Lecture Notes in Business Information Processing, 89–102. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-67781-7_9.
Full textStrekalova, Yulia A. "Online Advertising." In Encyclopedia of Big Data, 1–5. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32001-4_152-1.
Full textCharlesworth, Alan. "Advertising online." In Absolute Essentials of Digital Marketing, 55–68. New York : Routledge, 2020. | Series: Absolute essentials of business and economics: Routledge, 2020. http://dx.doi.org/10.4324/9781003015789-6.
Full textSpurgeon, Christina. "Online Advertising." In The Routledge Companion to Global Internet Histories, 387–98. New York: Routledge, Taylor & Francis Group, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315748962-27.
Full textEast, Robert. "Online Advertising." In The Effect of Advertising and Display, 85–91. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-23377-2_6.
Full textCharlesworth, Alan. "Advertising online." In Digital Marketing, 197–237. 3rd edition. | Abingdon, Oxon ; New York, NY : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315175737-7.
Full textWiktor, Jan W., and Katarzyna Sanak-Kosmowska. "Online advertising." In Information Asymmetry in Online Advertising, 3–32. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003134121-2.
Full textArmstrong, J. Scott. "Types of evidence." In Persuasive Advertising, 6–13. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230285804_2.
Full textConference papers on the topic "Types of online advertising"
Pan, Junwei, Yizhi Mao, Alfonso Lobos Ruiz, Yu Sun, and Aaron Flores. "Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330783.
Full text"Which Clicks Lead to Conversions? - Modeling User-journeys Across Multiple Types of Online Advertising." In International Conference on e-Business. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004504901410152.
Full textMaehara, Takanori, Atsuhiro Narita, Jun Baba, and Takayuki Kawabata. "Optimal Bidding Strategy for Brand Advertising." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/59.
Full textSurya, Desayu, and H. Hermawan. "Universitas Komputer Indonesia Students' Understanding of the Types of Online Advertising in Meeting Their Information Needs." In Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 18 July 2019, Bandung, Indonesia. EAI, 2019. http://dx.doi.org/10.4108/eai.18-7-2019.2287684.
Full textOuyang, Wentao, Xiuwu Zhang, Shukui Ren, Chao Qi, Zhaojie Liu, and Yanlong Du. "Representation Learning-Assisted Click-Through Rate Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/634.
Full textMüller, Lea. "Transforming Online Advertising." In SIGMIS-CPR '18: 2018 Computers and People Research Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209626.3209632.
Full textBhumgara, Afreen. "Online mobile targeted advertising." In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2015. http://dx.doi.org/10.1109/iccic.2015.7435684.
Full textGhosh, Arpita, and Mohammad Mahdian. "Externalities in online advertising." In Proceeding of the 17th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1367497.1367520.
Full textLiu, Dapeng, Shaochun Xu, Lichao Chen, and Chunning Wang. "Some observations on online advertising: A new advertising system." In 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS). IEEE, 2015. http://dx.doi.org/10.1109/icis.2015.7166625.
Full textSueiras, Jorge, Fausto Morales, and Juan-Carlos Ibanez. "From TV to online advertising." In the 1st international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1348599.1348609.
Full textReports on the topic "Types of online advertising"
Chesnes, Matthew, and Ginger Zhe Jin. Direct-to-Consumer Advertising and Online Search. Cambridge, MA: National Bureau of Economic Research, August 2016. http://dx.doi.org/10.3386/w22582.
Full textGrow, André, Daniela Perrotta, Emanuele Del Fava, Jorge Cimentada, Francesco Rampazzo, Beatriz Sofía Gil-Clavel, Emilio Zagheni, René D. Flores, Ilana Ventura, and Ingmar G. Weber. How reliable is Facebook’s advertising data for use in social science research? Insights from a cross-national online survey. Rostock: Max Planck Institute for Demographic Research, April 2021. http://dx.doi.org/10.4054/mpidr-wp-2021-006.
Full textRobayo Botiva, Diana María. Brief Current Context of the Types of Electronic Commerce in Colombia. Ediciones Universidad Cooperativa de Colombia, April 2021. http://dx.doi.org/10.16925/gclc.17.
Full textArbeit, Caren A., Alexander Bentz, Emily Forrest Cataldi, and Herschel Sanders. Alternative and Independent: The universe of technology-related “bootcamps". RTI Press, February 2019. http://dx.doi.org/10.3768/rtipress.2019.rr.0033.1902.
Full textMajchrowska, Justyna. TESTIMONIAL IN (NEW) MEDIA. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11109.
Full textHotsur, Oksana. SOCIAL NETWORKS AND BLOGS AS TOOLS PR-CAMPAIGN IMPLEMENTATIONS. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11110.
Full textGiles Álvarez, Laura, and Jeetendra Khadan. Mind the Gender Gap: A Picture of the Socioeconomic Trends Surrounding COVID-19 in the Caribbean with a Gender Lens. Inter-American Development Bank, December 2020. http://dx.doi.org/10.18235/0002961.
Full textTidd, Alexander N., Richard A. Ayers, Grant P. Course, and Guy R. Pasco. Scottish Inshore Fisheries Integrated Data System (SIFIDS): work package 6 final report development of a pilot relational data resource for the collation and interpretation of inshore fisheries data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23452.
Full textAgrawal, Asha Weinstein, Hilary Nixon, and Cameron Simmons. Investing in California’s Transportation Future: Public Opinion on Critical Needs. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1861.
Full textBrophy, Kenny, and Alison Sheridan, eds. Neolithic Scotland: ScARF Panel Report. Society of Antiquaries of Scotland, June 2012. http://dx.doi.org/10.9750/scarf.06.2012.196.
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