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dc.contributor.advisorPinzón Cadena, Liza Leonor
dc.contributor.authorUguerey Machado, Jhonathan Ramon
dc.date.accessioned2020-04-01T12:14:08Z
dc.date.available2020-04-01T12:14:08Z
dc.date.issued2020-03-02
dc.identifier.urihttp://hdl.handle.net/10818/40295
dc.description49 páginases_CO
dc.description.abstractLa era digital y su desarrollo actualmente está impactando diversas áreas de estudio, dentro de las cuales se encuentra el marketing y su participación en el mundo digital como Marketing Digital, donde el procesamiento de datos por medio del Big Data permiten obtener información relevante que pueda transformarse en conocimiento a través del Business Intelligence para construir más y mejores estrategias en los negocios. Este estudio tiene como objetivo analizar los documentos científicos desarrollados en el periodo comprendido entre el año 2000 al 2019 referente al Marketing Digital y la influencia del Big Data y Business Intelligence en el mismo, por medio de un análisis bibliométrico que comprende el análisis de rendimiento, donde se analizan indicadores como la producción de documentos y su citación, y el mapeo científico con el software VOS Viewer para analizar las redes de coocurrencia de términos y co-citaciones de autores, a partir de metadatos obtenidos en la base de datos Scopus, estos se basan en la información obtenida de documentos publicados en revistas científicas. El resultado del estudio muestra que la producción de documentos científicos en los últimos años ha venido aumentando, donde los Estados Unidos es el país con mayor influencia por su número de documentos y Reino Unido el país con más citaciones por documento, con un impacto significativo en materia de desarrollo científico y la estructura de los documentos se basa alrededor de 4 clústeres. Estos resultados pueden facilitar a la planificación, diseño, ejecución y publicación en futuras investigaciones sobre este tema.es_CO
dc.formatapplication/pdfes_CO
dc.language.isospaes_CO
dc.publisherUniversidad de La Sabanaes_CO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceUniversidad de La Sabana
dc.sourceIntellectum Repositorio Universidad de La Sabana
dc.subjectBig Dataes_CO
dc.subjectInteligencia de negocioses_CO
dc.subjectMercadeo en internetes_CO
dc.subjectPlanificación estratégicaes_CO
dc.titleBig data y el business intelligence en el marketing digital: un análisis bibliométricoes_CO
dc.typemasterThesises_CO
dc.publisher.programMaestría en Gerencia Internacionales_CO
dc.publisher.departmentEscuela Internacional de Ciencias Económicas y Administrativases_CO
dc.identifier.local276612
dc.identifier.localTE10530
dc.type.hasVersionacceptedVersiones_CO
dc.rights.accessRightsrestrictedAccesses_CO
dc.creator.degreeMagíster en Gerencia Internacionales_CO
dcterms.referencesAmirbagheri, K., Núñez-Carballosa, A., Guitart-Tarrés, L., & Merigó, J. M. (2019). Research on green supply chain: a bibliometric analysis. Clean Technologies & Environmental Policy, 21(1), 3–22. https://doi.org/10.1007/s10098-018-1624-1eng
dcterms.referencesBachmann, P., & Kantorová, K. (2016). From customer orientation to social CRM. New insights from central Europe. Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration, 23(36), 29–41.eng
dcterms.referencesBallestar, M. T., Grau-Carles, P., & Sainz, J. (2019). Predicting customer quality in e-commerce social networks: a machine learning approach. Review of Managerial Science, 13(3), 589– 603. https://doi.org/10.1007/s11846-018-0316-xeng
dcterms.referencesBengel, A., & Shawki, A. (2015). Simplifying Web Analytics for Digital Marketing, 1917–1918.eng
dcterms.referencesBerry, M. J. ., & Linoff, G. S. (2004). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. (John Wiley and Sons, Ed.) (2nd ed.). New York.eng
dcterms.referencesBheekharry, N. D., & Singh, U. (2019). Integrating information technology and marketing for better customer value. Springer Singapore. https://doi.org/10.1007/978-981-13-3338-5eng
dcterms.referencesChen, W., Zhang, Q., Jin, M., & Yang, J. (2019). Research on online consumer behavior and psychology under the background of big data. In Concurrency Computation (Vol. 31, pp. 1– 5). https://doi.org/10.1002/cpe.4852eng
dcterms.referencesChiang, W. Y. (2018). Identifying high-value airlines customers for strategies of online marketing systems: An empirical case in Taiwan. Kybernetes, 47(3), 525–538. https://doi.org/10.1108/K-12-2016-0348eng
dcterms.referencesChiang, W. Y. (2019). Establishing high value markets for data-driven customer relationship management systems: An empirical case study. Kybernetes, 48(3), 650–662. https://doi.org/10.1108/K-10-2017-0357eng
dcterms.referencesCibrián Barredo, I. (2019). Marketing digital : mide, analiza y mejora / Inés Cibrián Barredo.eng
dcterms.referencesClose, A. G., & Kukar-Kinney, M. (2010). Beyond buying: Motivations behind consumers’ online shopping cart use. Journal of Business Research, 63(9), 986–992. https://doi.org/10.1016/j.jbusres.2009.01.022eng
dcterms.referencesCobo, M. J., & Herrera, F. (2012). SciMAT : A New Science Mapping Analysis Software Tool. Journal of the American Society For Information Sciencie and Technology, 3(8), 1609–1630. https://doi.org/10.1002/asieng
dcterms.referencesCobo, M. J., Lpez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society For Information Sciencie and Technology. Retrieved from http://explore.bl.uk/primo_library/libweb/action/display.do?tabs=detailsTab&gathStatTab=t rue&ct=display&fn=search&doc=ETOCRN293395730&indx=1&recIds=ETOCRN293395 730eng
dcterms.referencesDavenport, T. H., & Harris, J. G. (2017). Competing on analytics : the new science of winning / Thomas H Davenport y Jeanne G Harris. Retrieved from http://unisabana.hosted.exlibrisgroup.com:80/F?func=service&doc_library=CNA01&local_ base=CNA01&doc_number=000267740&sequence=000001&line_number=0001&func_co de=DB_RECORDS&service_type=MEDIAeng
dcterms.referencesElsevier. (2019). The largest database of peer-reviewed literature. Retrieved October 3, 2019, from https://www.elsevier.com/solutions/scopuseng
dcterms.referencesFischbach, S., & Zarzosa, J. (2018). Big data on a smaller scale: A social media analytics assignment. Journal of Education for Business, 93(3), 142–148. https://doi.org/10.1080/08832323.2018.1433123eng
dcterms.referencesFleder, D., & Hosanagar, K. (2009). Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management Science, 55(5), 697. https://doi.org/10.1287/mnsc.1080.0974eng
dcterms.referencesGartner. (2001). IT Glossary. Retrieved from https://www.gartner.com/it-glossary/big-data/eng
dcterms.referencesGaviria-Marin, M., Merigó, J. M., & Baier-Fuentes, H. (2019). Knowledge management: A global examination based on bibliometric analysis. Technological Forecasting & Social Change, 140, 194–220. https://doi.org/10.1016/j.techfore.2018.07.006eng
dcterms.referencesGheorghe, S., Popescu, M., & Purcǎrea, A. A. (2017). A model of business intelligence and online marketing for commercial. In Balkan Region Conference on Engineering and Business Education (Vol. 3, pp. 267–274). https://doi.org/10.1515/cplbu-2017-0035eng
dcterms.referencesGoldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009). The Role of Hubs in the Adoption Process. Journal of Marketing, 73(2), 1–13. https://doi.org/10.1509/jmkg.73.2.1eng
dcterms.referencesGriffith, B. C., Small, H. G., Stonehill, J. A., & Dey, S. (1974). The Structure of Scientific Literatures II: Toward a Macro- and Microstructure for Science. Science Studies, 4(4), 339– 365. Retrieved from https://www.jstor.org/stable/284546eng
dcterms.referencesGutiérrez-Salcedo, M., Martínez, M. Á., Moral-Munoz, J. A., Herrera-Viedma, E., & Cobo, M. J. (2018). Some bibliometric procedures for analyzing and evaluating research fields. Applied Intelligence, 48(5), 1275–1287. https://doi.org/10.1007/s10489-017-1105-yeng
dcterms.referencesHan, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=e000xww&AN=377411&site=edsliveeng
dcterms.referencesHäubl, G., & Trifts, V. (2000). Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science, 19(1), 4–21. https://doi.org/10.1287/mksc.19.1.4.15178eng
dcterms.referencesHe, X., Dai, W., Cao, G., Tang, R., Yuan, M., & Yang, Q. (2015). Mining target users for online marketing based on App Store data. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, 1043–1052. https://doi.org/10.1109/BigData.2015.7363858eng
dcterms.referencesJedidi, K., & Kohli, R. (1996). Consideration Sets in Conjoint Analysis. Journal of Marketing Research (JMR), 33(3), 364–372. https://doi.org/10.2307/3152132eng
dcterms.referencesKannan, P. K., & Li, H. “Alice.” (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22–45. https://doi.org/10.1016/j.ijresmar.2016.11.006eng
dcterms.referencesKaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.003eng
dcterms.referencesKhan, M., Krishnamoorthy, N., Jalali, L., & Biswas, R. (2019). Adobe Identity Graph. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 5354–5356. https://doi.org/10.1109/BigData.2018.8622009eng
dcterms.referencesKietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241–251. https://doi.org/10.1016/j.bushor.2011.01.005eng
dcterms.referencesKim, D., Park, S., & Ko, M. (2018). A Study on the Analysis of IT-related Occupational Cluster using Big Data.eng
dcterms.referencesKostoff, R. N., Tshiteya, R., Pfeil, K. M., Humenik, J. A., & Karypis, G. (2005). Power source roadmaps using bibliometrics and database tomography. Energy, 30(5), 709–730. https://doi.org/10.1016/j.energy.2004.04.058eng
dcterms.referencesKotler, P., & Armstrong, G. (2010). Principles of Marketing. World Wide Web Internet And Web Information Systems. https://doi.org/10.2307/1250103eng
dcterms.referencesKotler, P., & Keller, K. L. (2016). Marketing Management. (Person Publishing, Ed.) (15th ed.). New York: Person Publishingeng
dcterms.referencesKukar-Kinney, M., & Close, A. G. (2010). The determinants of consumers’ online shopping cart abandonment. Journal of the Academy of Marketing Science, 38(2), 240–250. https://doi.org/10.1007/s11747-009-0141-5eng
dcterms.referencesLeeflang, P. S. H., Verhoef, P. C., Dahlström, P., & Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1–12. https://doi.org/10.1016/j.emj.2013.12.001eng
dcterms.referencesLeyva, J., Chávez, J., Pinedo, F., & Niebla, J. (2019). Bibliometric analysis of Organizational culture in Business economics of Web of Science , 1980-2018. Nova Scientia, 11(1), 478– 500eng
dcterms.referencesLiu, Y., & Zhang, T. (2019). Research on digital marketing strategies of fast fashion clothing brands based on big data. Proceedings - 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2019, 552–556. https://doi.org/10.1109/YAC.2019.8787647eng
dcterms.referencesMarshakova, I. (1973). System of Document Connections Based on References. NauchnTechn.Inform., 2(6), 3–8.eng
dcterms.referencesMascarenhas, C., & Marques, C. S. (2017). Entrepreneurial university : towards a better understanding of past trends and future directions. Journal of Enterprising Communities: People and Places in the Global Economy., 11(3), 316–338. https://doi.org/10.1108/JEC-02- 2017-0019eng
dcterms.referencesMazzei, M. (2019). Web 2.0. In Salem Press Encyclopedia of Science.eng
dcterms.referencesMerigó, J. M., Muller, C., Modak, N. M., & Laengle, S. (2019). Research in Production and Operations Management: A University-Based Bibliometric Analysis. Global Journal of Flexible Systems Management, 20(1), 1–29. https://doi.org/10.1007/s40171-018-0201-0eng
dcterms.referencesMerigó, J. M., & Yang, J. (2017). A bibliometric analysis of operations research and. Omega, 73, 37–48. https://doi.org/10.1016/j.omega.2016.12.004eng
dcterms.referencesMiklosik, A., Kuchta, M., Evans, N., & Zak, S. (2019). Towards the Adoption of Machine Learning-Based Analytical Tools in Digital Marketing. IEEE Access, 7, 85705–85718. https://doi.org/10.1109/ACCESS.2019.2924425eng
dcterms.referencesMndebele, Z. N., & Ramachandran, M. (2017). IoT based proximity marketing. IoTBDS 2017 - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security, (IoTBDS), 325–330. https://doi.org/10.5220/0006347903250330eng
dcterms.referencesMoe, W. W. (2006). An Empirical Two-Stage Choice Model with Varying Decision Rules Applied to Internet Clickstream Data. Journal of Marketing Research (JMR), 43(4), 680–692. https://doi.org/10.1509/jmkr.43.4.680eng
dcterms.referencesMoe, W. W., & Fader, P. S. (2004). Dynamic Conversion Behavior at E-Commerce Sites. Management Science, 50(3), 326. https://doi.org/10.1287/mnsc.1040.0153eng
dcterms.referencesMuñoz-Villamizar, A., Solano, E., Quintero-Araujo, C., & Santos, J. (2019). Sustainability and digitalization in supply chains: A bibliometric analysis. Uncertain Supply Chain Management, 7, 703–712. https://doi.org/10.5267/j.uscm.2019.3.002eng
dcterms.referencesNadler, A., & McGuigan, L. (2018). An impulse to exploit: the behavioral turn in data-driven marketing. Critical Studies in Media Communication, 35(2), 151–165. https://doi.org/10.1080/15295036.2017.1387279eng
dcterms.referencesNolan, S., & Dane, A. (2018). A sharper conversation: book publishers’ use of social media marketing in the age of the algorithm. Media International Australia, 168(1), 153–166. https://doi.org/10.1177/1329878X18783008eng
dcterms.referencesNoyons, E. C. M., & Moed, H. F. (1999). Combining Mapping and Citation Analysis for Evaluative Bibliometric Purposes : A Bibliometric Study. Journal of the American Society For Information Sciencie, 50(2), 115–131.eng
dcterms.referencesOklander, M., Oklander, T., Yashkina, O., Pedko, I., & Chaikovska, M. (2018). Analysis of technological innovations in digital marketing. Eastern-European Journal of Enterprise Technologies, 5(3–95), 80–91. https://doi.org/10.15587/1729-4061.2018.143956eng
dcterms.referencesPérez Marqués, M. (2015). Business intelligence : técnicas, herramientas y aplicaciones / María Pérez Marqués.eng
dcterms.referencesPINEDA OSPINA, D. L. (2015). Bibliometric analysis for the identification of factors of innovation in the food industry. AD-Minister, (27), 95–126. https://doi.org/10.17230/administer.27.5eng
dcterms.referencesPourkhani, A., Abdipour, K., Baher, B., & Moslehpour, M. (2019). The impact of social media in business growth and performance: A scientometrics analysis. International Journal of Data and Network Science, 3, 223–244. https://doi.org/10.5267/j.ijdns.2019.2.003eng
dcterms.referencesPritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25, 348.eng
dcterms.referencesRamos, C. M. Q., Matos, N., Sousa, C. M. R., Correia, M. B., & Cascada, P. (2017). Marketing Intelligence and Automation – An Approach Associated with Tourism in Order to Obtain Economic Bene fi ts for a Region (Vol. 2). Springer International Publishing.https://doi.org/10.1007/978-3-319-58706-6eng
dcterms.referencesRavi, A., Sangaralingam, K., & Datta, A. (2019). Predicting Consumer Level Brand Preferences Using Persistent Mobility Patterns. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 1986–1991. https://doi.org/10.1109/BigData.2018.8622225eng
dcterms.referencesSantosh, K. C., De Sarkar, S., & Mukherjee, A. (2018). Product popularity modeling via time series embedding. In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 (pp. 650–653). IEEE. https://doi.org/10.1109/ASONAM.2018.8508291eng
dcterms.referencesScimago Journal & Country Rank. (2019). Journal Rankings. Retrieved November 26, 2019, from https://www.scimagojr.com/eng
dcterms.referencesScopus. (2019). Affiliation details. Retrieved November 29, 2019, from https://www-scopuscom.ez.unisabana.edu.co/affil/profile.uri?afid=60076047&origin=resultsAnalyzer&zone=af filiationNameeng
dcterms.referencesSingh, S. P., & Solanki, S. (2019). A Movie Recommender System Using Modified Cuckoo Search. In Lecture Notes in Electrical Engineering (Vol. 545, pp. 471–482). Springer Singapore. https://doi.org/10.1007/978-981-13-5802-9_43eng
dcterms.referencesSmall, H. (1973). Co-citation in the Scientific Literature: A New Measure of the Relationship Between Two Documents. Journal of the American Society for Information Science, 24(4), 265–269. https://doi.org/10.1002/asi.4630240406eng
dcterms.referencesSmall, H. (1999). Visualizing Science by Citation Mapping. Journal of the American Society For Information Sciencie, 50(1973), 799–813.eng
dcterms.referencesSong, G.-Y., Cheon, Y., Lee, K., Park, K. M., & Rim, H.-C. (2014). Inter-category Map: Building Cognition Network of General Customers through Big Data Mining. KSII Transactions on Internet and Information Systems : TIIS, 8(2), 583. Retrieved from http://click.ndsl.kr/servlet/LinkingDetailView?cn=JAKO201409841770203&dbt=JAKO&o rg_code=O483&site_code=SS1483&service_code=01eng
dcterms.referencesStange, M., & Funk, B. (2015). How much tracking is necessary? - The learning curve in Bayesian user journey analysis. In 23rd European Conference on Information Systems, ECIS 2015 (Vol. 2015-May, pp. 0–15).eng
dcterms.referencesTang, J., Gao, H., Hu, X., & Liu, H. (2013). Context-aware review helpfulness rating prediction. In Proceedings of the 7th ACM Conference Recommender Systems (pp. 1–8). https://doi.org/10.1145/2507157.2507183eng
dcterms.referencesThelwall, M. (2009). Bibliometrics and Citation Analysis: From the Science Citation Index to Cybermetrics. By Nicola De Bellis. Lanham, MD: Scarecrow, 2009. 415pp. $55 (pbk). ISBN 978-0-8108-6713-0 (pbk). Library and Information Science Research. https://doi.org/10.1016/j.lisr.2009.04.002eng
dcterms.referencesTranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing EvidenceInformed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375eng
dcterms.referencesTrusov, M., Ma, L., & Jamal, Z. (2016). Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Marketing Science, 35(3), 405–426. https://doi.org/10.1287/mksc.2015.0956eng
dcterms.referencesvan Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009- 0146-3eng
dcterms.referencesvan Eck, N. J., & Waltman, L. (2014). Visualizing Bibliometric Networks. Measuring Scholarly Impact. https://doi.org/10.1007/978-3-319-10377-8_13eng
dcterms.referencesVerhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer engagement as a new perspective in customer management. Journal of Service Research, 13(3), 247–252. https://doi.org/10.1177/1094670510375461eng
dcterms.referencesWang, H., Wei, Q., & Chen, G. (2013). From clicking to consideration: A business intelligence approach to estimating consumers’ consideration probabilities. Decision Support Systems, 56(1), 397–405. https://doi.org/10.1016/j.dss.2012.10.052eng
dcterms.referencesWright, L. T., Robin, R., Stone, M., & Aravopoulou, D. E. (2019). Adoption of Big Data Technology for Innovation in B2B Marketing. Journal of Business-to-Business Marketing, 00(00), 1–13. https://doi.org/10.1080/1051712X.2019.1611082eng
dcterms.referencesWymbs, C. (2011). Digital marketing: The time for a new “academic major” has arrived. Journal of Marketing Education, 33(1), 93–106. https://doi.org/10.1177/0273475310392544eng
dcterms.referencesYang, K. (2015). Applying Reinforcement Theory to Implementing a Retargeting Advertising in the Electronic Commerce Website.eng
dcterms.referencesZemigala, M. (2019). Tendencies in research on sustainable development in management sciences. Journal of Cleaner Production, 218, 796–809. https://doi.org/10.1016/j.jclepro.2019.02.009eng


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