dc.contributor.advisor | Pinzón Cadena, Liza Leonor | |
dc.contributor.author | Uguerey Machado, Jhonathan Ramon | |
dc.date.accessioned | 2020-04-01T12:14:08Z | |
dc.date.available | 2020-04-01T12:14:08Z | |
dc.date.issued | 2020-03-02 | |
dc.identifier.uri | http://hdl.handle.net/10818/40295 | |
dc.description | 49 páginas | es_CO |
dc.description.abstract | La 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.format | application/pdf | es_CO |
dc.language.iso | spa | es_CO |
dc.publisher | Universidad de La Sabana | es_CO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | Universidad de La Sabana | |
dc.source | Intellectum Repositorio Universidad de La Sabana | |
dc.subject | Big Data | es_CO |
dc.subject | Inteligencia de negocios | es_CO |
dc.subject | Mercadeo en internet | es_CO |
dc.subject | Planificación estratégica | es_CO |
dc.title | Big data y el business intelligence en el marketing digital: un análisis bibliométrico | es_CO |
dc.type | masterThesis | es_CO |
dc.publisher.program | Maestría en Gerencia Internacional | es_CO |
dc.publisher.department | Escuela Internacional de Ciencias Económicas y Administrativas | es_CO |
dc.identifier.local | 276612 | |
dc.identifier.local | TE10530 | |
dc.type.hasVersion | acceptedVersion | es_CO |
dc.rights.accessRights | restrictedAccess | es_CO |
dc.creator.degree | Magíster en Gerencia Internacional | es_CO |
dcterms.references | Amirbagheri, 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/s10098-018-1624-1 | eng |
dcterms.references | Bachmann, 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.references | Ballestar, 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-x | eng |
dcterms.references | Bengel, A., & Shawki, A. (2015). Simplifying Web Analytics for Digital Marketing, 1917–1918. | eng |
dcterms.references | Berry, 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.references | Bheekharry, N. D., & Singh, U. (2019). Integrating information technology and marketing for
better customer value. Springer Singapore. https://doi.org/10.1007/978-981-13-3338-5 | eng |
dcterms.references | Chen, 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.4852 | eng |
dcterms.references | Chiang, 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-12-2016-0348 | eng |
dcterms.references | Chiang, 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-10-2017-0357 | eng |
dcterms.references | Cibrián Barredo, I. (2019). Marketing digital : mide, analiza y mejora / Inés Cibrián Barredo. | eng |
dcterms.references | Close, 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.2009.01.022 | eng |
dcterms.references | Cobo, 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/asi | eng |
dcterms.references | Cobo, 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
730 | eng |
dcterms.references | Davenport, 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=MEDIA | eng |
dcterms.references | Elsevier. (2019). The largest database of peer-reviewed literature. Retrieved October 3, 2019, from
https://www.elsevier.com/solutions/scopus | eng |
dcterms.references | Fischbach, 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/10.1080/08832323.2018.1433123 | eng |
dcterms.references | Fleder, 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.1080.0974 | eng |
dcterms.references | Gartner. (2001). IT Glossary. Retrieved from https://www.gartner.com/it-glossary/big-data/ | eng |
dcterms.references | Gaviria-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.2018.07.006 | eng |
dcterms.references | Gheorghe, 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-2017-0035 | eng |
dcterms.references | Goldenberg, 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.73.2.1 | eng |
dcterms.references | Griffith, 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/284546 | eng |
dcterms.references | Gutiérrez-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-y | eng |
dcterms.references | Han, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques. Retrieved from
http://search.ebscohost.com/login.aspx?direct=true&db=e000xww&AN=377411&site=edslive | eng |
dcterms.references | Häubl, 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.19.1.4.15178 | eng |
dcterms.references | He, 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.2015.7363858 | eng |
dcterms.references | Jedidi, K., & Kohli, R. (1996). Consideration Sets in Conjoint Analysis. Journal of Marketing
Research (JMR), 33(3), 364–372. https://doi.org/10.2307/3152132 | eng |
dcterms.references | Kannan, 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.2016.11.006 | eng |
dcterms.references | Kaplan, 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.2009.09.003 | eng |
dcterms.references | Khan, 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.2018.8622009 | eng |
dcterms.references | Kietzmann, 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.2011.01.005 | eng |
dcterms.references | Kim, D., Park, S., & Ko, M. (2018). A Study on the Analysis of IT-related Occupational Cluster
using Big Data. | eng |
dcterms.references | Kostoff, 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.2004.04.058 | eng |
dcterms.references | Kotler, P., & Armstrong, G. (2010). Principles of Marketing. World Wide Web Internet And Web
Information Systems. https://doi.org/10.2307/1250103 | eng |
dcterms.references | Kotler, P., & Keller, K. L. (2016). Marketing Management. (Person Publishing, Ed.) (15th ed.).
New York: Person Publishing | eng |
dcterms.references | Kukar-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/s11747-009-0141-5 | eng |
dcterms.references | Leeflang, 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.2013.12.001 | eng |
dcterms.references | Leyva, 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–
500 | eng |
dcterms.references | Liu, 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.2019.8787647 | eng |
dcterms.references | Marshakova, I. (1973). System of Document Connections Based on References. NauchnTechn.Inform., 2(6), 3–8. | eng |
dcterms.references | Mascarenhas, 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-
2017-0019 | eng |
dcterms.references | Mazzei, M. (2019). Web 2.0. In Salem Press Encyclopedia of Science. | eng |
dcterms.references | Merigó, 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/s40171-018-0201-0 | eng |
dcterms.references | Merigó, J. M., & Yang, J. (2017). A bibliometric analysis of operations research and. Omega, 73,
37–48. https://doi.org/10.1016/j.omega.2016.12.004 | eng |
dcterms.references | Miklosik, 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.2019.2924425 | eng |
dcterms.references | Mndebele, 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/10.5220/0006347903250330 | eng |
dcterms.references | Moe, 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.43.4.680 | eng |
dcterms.references | Moe, W. W., & Fader, P. S. (2004). Dynamic Conversion Behavior at E-Commerce Sites.
Management Science, 50(3), 326. https://doi.org/10.1287/mnsc.1040.0153 | eng |
dcterms.references | Muñoz-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.2019.3.002 | eng |
dcterms.references | Nadler, 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/10.1080/15295036.2017.1387279 | eng |
dcterms.references | Nolan, 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/10.1177/1329878X18783008 | eng |
dcterms.references | Noyons, 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.references | Oklander, 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/10.15587/1729-4061.2018.143956 | eng |
dcterms.references | Pérez Marqués, M. (2015). Business intelligence : técnicas, herramientas y aplicaciones / María
Pérez Marqués. | eng |
dcterms.references | PINEDA 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.27.5 | eng |
dcterms.references | Pourkhani, 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.2019.2.003 | eng |
dcterms.references | Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25, 348. | eng |
dcterms.references | Ramos, 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/10.1007/978-3-319-58706-6 | eng |
dcterms.references | Ravi, 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.2018.8622225 | eng |
dcterms.references | Santosh, 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.2018.8508291 | eng |
dcterms.references | Scimago Journal & Country Rank. (2019). Journal Rankings. Retrieved November 26, 2019, from
https://www.scimagojr.com/ | eng |
dcterms.references | Scopus. (2019). Affiliation details. Retrieved November 29, 2019, from https://www-scopuscom.ez.unisabana.edu.co/affil/profile.uri?afid=60076047&origin=resultsAnalyzer&zone=af
filiationName | eng |
dcterms.references | Singh, 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_43 | eng |
dcterms.references | Small, 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.4630240406 | eng |
dcterms.references | Small, H. (1999). Visualizing Science by Citation Mapping. Journal of the American Society For
Information Sciencie, 50(1973), 799–813. | eng |
dcterms.references | Song, 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=01 | eng |
dcterms.references | Stange, 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.references | Tang, 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/10.1145/2507157.2507183 | eng |
dcterms.references | Thelwall, 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.2009.04.002 | eng |
dcterms.references | Tranfield, 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/10.1111/1467-8551.00375 | eng |
dcterms.references | Trusov, 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.2015.0956 | eng |
dcterms.references | van 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-
0146-3 | eng |
dcterms.references | van Eck, N. J., & Waltman, L. (2014). Visualizing Bibliometric Networks. Measuring Scholarly Impact. https://doi.org/10.1007/978-3-319-10377-8_13 | eng |
dcterms.references | Verhoef, 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/10.1177/1094670510375461 | eng |
dcterms.references | Wang, 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.2012.10.052 | eng |
dcterms.references | Wright, 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.2019.1611082 | eng |
dcterms.references | Wymbs, C. (2011). Digital marketing: The time for a new “academic major” has arrived. Journal
of Marketing Education, 33(1), 93–106. https://doi.org/10.1177/0273475310392544 | eng |
dcterms.references | Yang, K. (2015). Applying Reinforcement Theory to Implementing a Retargeting Advertising in
the Electronic Commerce Website. | eng |
dcterms.references | Zemigala, 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.2019.02.009 | eng |