Optimización empresarial mediante big data para la personalización de estrategias en pymes: una revisión narrativa
DOI:
https://doi.org/10.31637/epsir-2024-579Palabras clave:
Big data, perzonalización, pymes, eficiencia operativa, sostenibilidad, soluciones prácticas, estrategias, desafíosResumen
Introducción: Este artículo examina el impacto de big data en la personalización de estrategias en pymes, destacando su importancia para prever las preferencias de los clientes y mejorar la personalización de productos y servicios. Sin embargo, las pymes enfrentan desafíos significativos que deben abordarse para maximizar estos beneficios. Metodología: Se realizó una revisión bibliográfica utilizando la base de datos de Scopus, enfocándose en estudios que abordan la implementación y uso de big data en pymes, publicados en inglés y español. Resultados: La revisión identificó desafíos como altos costos de implementación, falta de habilidades técnicas, problemas de privacidad y resistencia al cambio. Se propusieron soluciones prácticas como el uso de soluciones en la nube, programas de capacitación, colaboración con instituciones educativas y estrategias robustas de gestión de datos. Las estrategias de personalización basadas en big data mejoran la eficiencia operativa, la toma de decisiones y la sostenibilidad a largo plazo de las pymes. Discusión: A pesar de los desafíos, existen oportunidades sustanciales para que las pymes optimicen sus procesos mediante big data. El respaldo ejecutivo, la formación pertinente y el acceso a tecnologías apropiadas son claves para la adopción de big data. Se recomienda futuras investigaciones empíricas y longitudinales, explorando enfoques interdisciplinarios que incluyan la psicología del consumidor y la economía digital.
Descargas
Citas
Ajah, I. A. y Nweke, H. F. (2019). Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications. Big Data and Cognitive Computing, 3(2), 32. https://doi.org/10.3390/bdcc3020032 DOI: https://doi.org/10.3390/bdcc3020032
Akter, S., Hossain, M. A., Lu, Q. y Shams, S. M. R. (2021). Big data-driven strategic orientation in international marketing. International Marketing Review, 38(5), 927-947. https://doi.org/10.1108/IMR-11-2020-0256 DOI: https://doi.org/10.1108/IMR-11-2020-0256
Aldossari, S., Mokhtar, U. A. y Abdul Ghani, A. T. (2023). Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review. SAGE Open, 13(4). https://doi.org/10.1177/21582440231217902 DOI: https://doi.org/10.1177/21582440231217902
Aljabhan, B. y Abeyie, M. (2022). Big Data Analytics in Supply Chain Management: A Qualitative Study. Computational Intelligence and Neuroscience, 1, 1-10. https://doi.org/10.1155/2022/9573669 DOI: https://doi.org/10.1155/2022/9573669
Anshari, M., Almunawar, M. N., Lim, S. A. y Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101. https://doi.org/10.1016/j.aci.2018.05.004 DOI: https://doi.org/10.1016/j.aci.2018.05.004
Aponte Franco, L. G. y Guerrero Castañeda, R. F. (2022). Revisión sistemática en investigación: Metodología y aplicación. Revista Científica, 34(2), 123-137.
Arksey, H. y O’Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19-32. https://doi.org/10.1080/1364557032000119616 DOI: https://doi.org/10.1080/1364557032000119616
Austin, C. C. (2018). A Path to Big Data Readiness. En 2018 IEEE International Conference on Big Data (Big Data) (pp. 4844-4853). https://doi.org/10.1109/BigData.2018.8622229 DOI: https://doi.org/10.1109/BigData.2018.8622229
Azmoodeh, A. y Dehghantanha, A. (2020). Big Data and Privacy: Challenges and Opportunities. En K. K. R. Choo y A. Dehghantanha (Eds.), Handbook of Big Data Privacy (pp. 1-5). Springer International Publishing. https://acortar.link/0iJZSR DOI: https://doi.org/10.1007/978-3-030-38557-6_1
Bag, S., Wood, L. C., Xu, L., Dhamija, P. y Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153, 104559. https://doi.org/10.1016/j.resconrec.2019.104559 DOI: https://doi.org/10.1016/j.resconrec.2019.104559
Bhaskaraputra, A., Sutojo, F., Ramadhan, A. N. y Agung Santoso Gunawan, A. (2022). Systematic Literature Review on Solving Personalization Problem in Digital Marketing using Machine Learning and Its Impact. En International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 178-182). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/iSemantic55962.2022.9920387 DOI: https://doi.org/10.1109/iSemantic55962.2022.9920387
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F. y Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225 DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102225
Bouwman, H., Nikou, S. y de Reuver, M. (2019). Digitalization, business models, and SMEs: How do business model innovation practices improve performance of digitalizing SMEs? Telecommunications Policy, 43(9), 101828. https://doi.org/10.1016/j.telpol.2019.101828 DOI: https://doi.org/10.1016/j.telpol.2019.101828
Ciampi, F., Marzi, G., Demi, S. y Faraoni, M. (2020). The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives. Journal of Knowledge Management, 24(5), 1157-1176. https://doi.org/10.1108/JKM-02-2020-0156 DOI: https://doi.org/10.1108/JKM-02-2020-0156
Corbett, C. J. (2018). How Sustainable Is big data? Production and Operations Management, 27(9), 1685-1695. https://doi.org/10.1111/poms.12837 DOI: https://doi.org/10.1111/poms.12837
Chaudhary, R., Aujla, G. S., Kumar, N. y Rodrigues, J. J. P. C. (2018). Optimized Big Data Management Across Multi-Cloud Data Centers: Software-Defined-Network-Based Analysis. IEEE Communications Magazine, 56(2), 118-126. https://doi.org/10.1109/MCOM.2018.1700211 DOI: https://doi.org/10.1109/MCOM.2018.1700211
Chen, H., Chiang, R. H. y Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188. DOI: https://doi.org/10.2307/41703503
Chi, C., Liu, T., Yu, X., Zhang, S. y Shi, S. (2019). Research on the Security of Personal Information in the Era of Big Data. En Artificial Intelligence for Communications and Networks: First EAI International Conference, AICON 2019, Harbin, China, May 25–26, 2019, Proceedings, Part II 1 (pp. 107-114). Springer International Publishing. https://doi.org/10.1007/978-3-030-22971-9_9 DOI: https://doi.org/10.1007/978-3-030-22971-9_9
Choi, T. M., Wallace, S. W. y Wang, Y. (2018). Big Data Analytics in Operations Management. Production and Operations Management, 27(10), 1868-1883. https://doi.org/10.1111/poms.12838 DOI: https://doi.org/10.1111/poms.12838
Chuah, M. H. y Thurusamry, R. (2021). Challenges of big data adoption in Malaysia SMEs based on Lessig’s modalities: A systematic review. Cogent Business & Management, 8(1), 1968191. https://doi.org/10.1080/23311975.2021.1968191 DOI: https://doi.org/10.1080/23311975.2021.1968191
Chuah, M. H. y Thurusamry, R. (2022). The relationship between architecture, social, law and market in determine challenges of big data analysis for Malaysia SMEs. Cogent Business & Management, 9(1), 2021835. https://doi.org/10.1080/23311975.2021.2021835 DOI: https://doi.org/10.1080/23311975.2021.2021835
Dam, N. A. K., Le Dinh, T. y Menvielle, W. (2019). A systematic literature review of big data adoption in internationalization. Journal of Marketing Analytics, 7(3), 182-195. https://doi.org/10.1057/s41270-019-00054-7 DOI: https://doi.org/10.1057/s41270-019-00054-7
Del Vecchio, P., Di Minin, A., Petruzzelli, A. M., Panniello, U. y Pirri, S. (2018). Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges. Creativity and Innovation Management, 27(1), 6-22. https://doi.org/10.1111/caim.12224 DOI: https://doi.org/10.1111/caim.12224
Dong, J. Q. y Yang, C. H. (2020). Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), 103124. https://doi.org/10.1016/j.im.2018.11.001 DOI: https://doi.org/10.1016/j.im.2018.11.001
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F. y Roubaud, D. (2019). Can big data and predictive analytics improve social and environmental sustainability? Technological Forecasting and Social Change, 144, 534-545. https://doi.org/10.1016/j.techfore.2017.06.020 DOI: https://doi.org/10.1016/j.techfore.2017.06.020
Er, C. H. y Mosawi, T. A. (2022). Effects of Big Data Analytics on Sustainable Manufacturing: A Comparative Study Analysis. Chinese Journal of Urban and Environmental Studies, 10(4), 1-25. https://doi.org/10.1142/S2345748122500221 DOI: https://doi.org/10.1142/S2345748122500221
Galetsi, P., Katsaliaki, K. y Kumar, S. (2019). Values, challenges and future directions of big data analytics in healthcare: A systematic review. Social Science & Medicine, 241, 112533. https://doi.org/10.1016/j.socscimed.2019.112533 DOI: https://doi.org/10.1016/j.socscimed.2019.112533
Gandomi, A. y Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. DOI: https://doi.org/10.1016/j.ijinfomgt.2014.10.007
García Remeseiro, T., Gutiérrez-Sánchez, A. y Alonso-Fernández, D. (2019). Interrater and intrarater reliability of the Postural Assessment Software (PAS/SAPO): A systematic review. Revista Andaluza de Medicina del Deporte, 12(3), 286-290. https://doi.org/10.33155/j.ramd.2018.02.006 DOI: https://doi.org/10.33155/j.ramd.2018.02.006
Gardiner, A., Aasheim, C., Rutner, P. y Williams, S. (2018). Skill Requirements in Big Data: A Content Analysis of Job Advertisements. Journal of Computer Information Systems, 58(4), 374-384. https://doi.org/10.1080/08874417.2017.1289354 DOI: https://doi.org/10.1080/08874417.2017.1289354
Goi, C. L. (2022). The Use of Big Data in Marketing Analytics. En I. Management Association (Ed.), Research Anthology on big data Analytics, Architectures, and Applications (pp. 1371-1387). IGI Global. https://acortar.link/Xts6Zo DOI: https://doi.org/10.4018/978-1-6684-3662-2.ch066
Gu, J. (2022). Research on precision marketing strategy and personalized recommendation method based on big data drive. Wireless Communications and Mobile Computing, 1. https://doi.org/10.1155/2022/6751413 DOI: https://doi.org/10.1155/2022/6751413
Guo, X. y Yuan, K. (2021). Promotion of Marketing Efficiency of SMEs Based on Big Data. En Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing (BIC '21) (pp. 244-249). Association for Computing Machinery. https://doi.org/10.1145/3448748.3448787 DOI: https://doi.org/10.1145/3448748.3448787
Hajjaji, Y., Boulila, W., Farah, I. R., Romdhani, I. y Hussain, A. (2021). Big data and IoT-based applications in smart environments: A systematic review. Computer Science Review, 39, 100318. https://doi.org/10.1016/j.cosrev.2020.100318 DOI: https://doi.org/10.1016/j.cosrev.2020.100318
Iqbal, M., Kazmi, S. H. A., Manzoor, A., Soomrani, A. R., Butt, S. H. y Shaikh, K. A. (2018). A study of big data for business growth in SMEs: Opportunities & challenges. En 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-7). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICOMET.2018.8346368 DOI: https://doi.org/10.1109/ICOMET.2018.8346368
Jayashankar, P., Johnston, W. J., Nilakanta, S. y Burres, R. (2020). Co-creation of value-in-use through big data technology- a B2B agricultural perspective. Journal of Business & Industrial Marketing, 35(3), 508-523. https://doi.org/10.1108/JBIM-12-2018-0411 DOI: https://doi.org/10.1108/JBIM-12-2018-0411
Johnson, B. T. y Hennessy, E. A. (2019). Systematic reviews and meta-analyses in the health sciences: Best practice methods for research syntheses. Social Science & Medicine, 233, 237-251. https://doi.org/10.1016/j.socscimed.2019.05.035 DOI: https://doi.org/10.1016/j.socscimed.2019.05.035
Kaisler, S., Armour, F., Espinosa, J. y Money, W. (2013). Big Data: Issues and Challenges Moving Forward. En 2013 46th Hawaii International Conference on System Sciences (pp. 995-1004). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/HICSS.2013.645 DOI: https://doi.org/10.1109/HICSS.2013.645
Kamel, M. A. (2023). Big data analytics and market performance: the roles of customization and personalization strategies and competitive intensity. Journal of Enterprise Information Management, 36(6), 1727-1749. https://doi.org/10.1108/JEIM-04-2022-0114 DOI: https://doi.org/10.1108/JEIM-04-2022-0114
Khanra, S., Dhir, A., Islam, A. K. M. N. y Mäntymäki, M. (2020). Big data analytics in healthcare: a systematic literature review. Enterprise Information Systems, 14(7), 878-912. https://doi.org/10.1080/17517575.2020.1812005 DOI: https://doi.org/10.1080/17517575.2020.1812005
Kitchens, B., Dobolyi, D., Li, J. y Abbasi, A. (2018). Advanced Customer Analytics: Strategic Value Through Integration of Relationship-Oriented Big Data. Journal of Management Information Systems, 35(2), 540-574. https://doi.org/10.1080/07421222.2018.1451957 DOI: https://doi.org/10.1080/07421222.2018.1451957
Koman, G., Tumová, D., Jankal, R. y Mičiak, M. (2022). Business-making supported via the application of big data to achieve economic sustainability. Entrepreneurship and Sustainability Issues, 9(4), 336-358. https://doi.org/10.9770/jesi.2022.9.4(18) DOI: https://doi.org/10.9770/jesi.2022.9.4(18)
Kong, L., Liu, Z. y Wu, J. (2020). A systematic review of big data-based urban sustainability research: State-of-the-science and future directions. Journal of Cleaner Production, 273, 123142. https://doi.org/10.1016/j.jclepro.2020.123142 DOI: https://doi.org/10.1016/j.jclepro.2020.123142
Kumar, N., Kumar, G. y Singh, R. K. (2021). Big data analytics application for sustainable manufacturing operations: analysis of strategic factors. Clean Technologies and Environmental Policy, 23(3), 965-989. https://doi.org/10.1007/s10098-020-02008-5 DOI: https://doi.org/10.1007/s10098-020-02008-5
Lee, I. y Mangalaraj, G. (2022). Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions. Big Data and Cognitive Computing, 6(1), 17. https://doi.org/10.3390/bdcc6010017 DOI: https://doi.org/10.3390/bdcc6010017
Leung, C. K., Kajal, A., Won, Y. y Choi, J. M. C. (2019). Big Data Analytics for Personalized Recommendation Systems. En IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) (pp. 1060-1065). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00190 DOI: https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00190
Levac, D., Colquhoun, H. y O'Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation Science, 5(1), 69. https://doi.org/10.1186/1748-5908-5-69 DOI: https://doi.org/10.1186/1748-5908-5-69
Liu, Y., Soroka, A., Han, L., Jian, J. y Tang, M. (2020). Cloud-based big data analytics for customer insight-driven design innovation in SMEs. International Journal of Information Management, 51, 102034. https://doi.org/10.1016/j.ijinfomgt.2019.11.002 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.11.002
Lozada, N., Arias-Pérez, J. y Perdomo-Charry, G. (2019). Big data analytics capability and co-innovation: An empirical study. Heliyon, 5(10), e02541. https://doi.org/10.1016/j.heliyon.2019.e02541 DOI: https://doi.org/10.1016/j.heliyon.2019.e02541
Lv, Z., Iqbal, R. y Chang, V. (2018). Big data analytics for sustainability. Future Generation Computer Systems, 86, 1238-1241. https://doi.org/10.1016/j.future.2018.05.020 DOI: https://doi.org/10.1016/j.future.2018.05.020
Maheshwari, S., Gautam, P. y Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875-1900. https://doi.org/10.1080/00207543.2020.1793011 DOI: https://doi.org/10.1080/00207543.2020.1793011
Majeed, A., Lv, J. y Peng, T. (2019). A framework for big data driven process analysis and optimization for additive manufacturing. Rapid Prototyping Journal, 25(2), 308-321. https://doi.org/10.1108/RPJ-04-2017-0075 DOI: https://doi.org/10.1108/RPJ-04-2017-0075
Mangla, S. K., Raut, R., Narwane, V. S., Zhang, Z. y priyadarshinee, P. (2021). Mediating effect of big data analytics on project performance of small and medium enterprises. Journal of Enterprise Information Management, 34(1), 168-198. https://acortar.link/WUQwoH DOI: https://doi.org/10.1108/JEIM-12-2019-0394
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. y Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
Maroufkhani, P., Wan Ismail, W. K. y Ghobakhloo, M. (2020). Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management, 11(4), 483-513. https://doi.org/10.1108/JSTPM-02-2020-0018 DOI: https://doi.org/10.1108/JSTPM-02-2020-0018
Masenya, T. M. (2023). Big Data Analytics as a Game Changer for Business Model Innovation in Small and Medium-Sized Enterprises in South Africa. International Journal of Innovation in the Digital Economy (IJIDE), 14(1), 1-17. https://doi.org/10.4018/IJIDE.323136 DOI: https://doi.org/10.4018/IJIDE.323136
Menaceur, S., Derdour, M. y Bouramoul, A. (2020). Using Query Expansion Techniques and Content-Based Filtering for Personalizing Analysis in Big Data. International Journal of Information Technology and Web Engineering (IJITWE), 15(2), 77-101. https://doi.org/10.4018/IJITWE.2020040104 DOI: https://doi.org/10.4018/IJITWE.2020040104
Mishra, H., Rautaray, S. S. y Pandey, M. (2023). Review on Big Data Analytics and its Impact on Marketing Strategy. En 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (pp. 424-429). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/I-SMAC58438.2023.10290469. DOI: https://doi.org/10.1109/I-SMAC58438.2023.10290469
Moher, D., Liberati, A., Tetzlaff, J. y Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Journal of clinical epidemiology, 62(10), 1006-1012. https://doi.org/10.1016/j.jclinepi.2009.06.005 DOI: https://doi.org/10.1016/j.jclinepi.2009.06.005
Mountasser, I., Ouhbi, B., Hdioud, F. y Frikh, B. (2021). Semantic-based big data integration framework using scalable distributed ontology matching strategy. Distributed and Parallel Databases, 39(4), 891-937. https://doi.org/10.1007/s10619-021-07321-6 DOI: https://doi.org/10.1007/s10619-021-07321-6
Noonpakdee, W., Phothichai, A. y Khunkornsiri, T. (2018). Big data implementation for small and medium enterprises. En 27th Wireless and Optical Communication Conference (WOCC) (pp. 1-5). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WOCC.2018.8372725 DOI: https://doi.org/10.1109/WOCC.2018.8372725
Persaud, A. (2021). Key competencies for big data analytics professions: a multimethod study. Information Technology & People, 34(1), 178-203. https://acortar.link/1K4VJO DOI: https://doi.org/10.1108/ITP-06-2019-0290
Qi, C. C. (2020). Big data management in the mining industry. International Journal of Minerals, Metallurgy and Materials, 27(2), 131-139. https://acortar.link/v3qT64 DOI: https://doi.org/10.1007/s12613-019-1937-z
Raghupathi, W. y Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3. DOI: https://doi.org/10.1186/2047-2501-2-3
Rajabion, L. (2018). Application and adoption of big data technologies in SMEs. En International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1133-1135). Institute of Electrical and Electronics Engineers. https://acortar.link/ClsPPG DOI: https://doi.org/10.1109/CSCI46756.2018.00219
Rakshit, S., Islam, N., Mondal, S. y Paul, T. (2021). Mobile apps for SME business sustainability during COVID-19 and onwards. Journal of Business Research, 135, 28-39. https://doi.org/10.1016/j.jbusres.2021.06.005 DOI: https://doi.org/10.1016/j.jbusres.2021.06.005
Ramadan, M., Shuqqo, H., Qtaishat, L., Asmar, H. y Salah, B. (2020). Sustainable competitive advantage driven by big data analytics and innovation. Applied Sciences, 10(19), 6784. https://doi.org/10.3390/app10196784 DOI: https://doi.org/10.3390/app10196784
Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P. y Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10-24. https://doi.org/10.1016/j.jclepro.2019.03.181 DOI: https://doi.org/10.1016/j.jclepro.2019.03.181
Sahoo, S. (2022). Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management. International Journal of Production Research, 60(22), 6793-6821. https://doi.org/10.1080/00207543.2021.1919333 DOI: https://doi.org/10.1080/00207543.2021.1919333
Samara, D., Magnisalis, I. y Peristeras, V. (2020). Artificial intelligence and big data in tourism: a systematic literature review. Journal of Hospitality and Tourism Technology, 11(2), 343-367. https://doi.org/10.1108/JHTT-12-2018-0118 DOI: https://doi.org/10.1108/JHTT-12-2018-0118
Sanchez-Hughet, C., Aramendia-Muneta, M. E. y Erro-Garcés, A. (2022). Seizing opportunities in Europe: a roadmap for efficient big data implementation in Spanish SMEs. Digital Policy, Regulation and Governance, 24(5), 463-478. https://doi.org/10.1108/DPRG-02-2022-0019 DOI: https://doi.org/10.1108/DPRG-02-2022-0019
Sanders, N. R. y Ganeshan, R. (2018). Big data in Supply Chain Management. Production and Operations Management, 27(10), 1745-1748. https://doi.org/10.1111/poms.12892 DOI: https://doi.org/10.1111/poms.12892
Sang, L., Yu, M., Lin, H., Zhang, Z. y Jin, R. (2021). Big data, technology capability and construction project quality: a cross-level investigation. Engineering, Construction and Architectural Management, 28(3), 706-727. https://doi.org/10.1108/ECAM-02-2020-0135 DOI: https://doi.org/10.1108/ECAM-02-2020-0135
Schaeffer, D. M. y Olson, P. C. (2014). Big data transforming small and medium enterprises. En M. Tavana y K. Puranam (Eds.), Handbook of Research on Organizational Transformations through big data Analytics (pp. 106-115). IGI Global. https://doi.org/10.4018/978-1-4666-7272-7.ch008 DOI: https://doi.org/10.4018/978-1-4666-7272-7.ch008
Sestino, A., Prete, M. I., Piper, L. y Guido, G. (2020). Internet of Things and Big Data as enablers for business digitalization strategies. Technovation, 98, 102173. https://doi.org/10.1016/j.technovation.2020.102173 DOI: https://doi.org/10.1016/j.technovation.2020.102173
Shabbir, M. Q. y Gardezi, S. B. W. (2020). Application of big data analytics and organizational performance: the mediating role of knowledge management practices. Journal of Big Data, 7(47). https://doi.org/10.1186/s40537-020-00317-6 DOI: https://doi.org/10.1186/s40537-020-00317-6
Shakhovska, N., Fedushko, S., Greguš ml, M., Melnykova, N., Shvorob, I. y Syerov, Y. (2019). Big data analysis in development of personalized medical system. Procedia Computer Science, 160, 229-234. https://doi.org/10.1016/j.procs.2019.09.461 DOI: https://doi.org/10.1016/j.procs.2019.09.461
Singh, S. K. y El-Kassar, A. N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213, 1264-1273. https://doi.org/10.1016/j.jclepro.2018.12.199 DOI: https://doi.org/10.1016/j.jclepro.2018.12.199
Steinberg, E. (2020). Big data and Personalized Pricing. Business Ethics Quarterly, 30(1), 97-117. https://doi.org/10.1017/beq.2019.19 DOI: https://doi.org/10.1017/beq.2019.19
Tabesh, P., Mousavidin, E. y Hasani, S. (2019). Implementing big data strategies: A managerial perspective. Business Horizons, 62(3), 347-358. https://doi.org/10.1016/j.bushor.2019.02.001 DOI: https://doi.org/10.1016/j.bushor.2019.02.001
Tarmidi, D. y Taruna, I. (2023). Big data analytics and key success factor in achieving competitive advantage and performance of small medium enterprises: literature review. Enrichment: Journal of Management, 13(1), 586-595. https://doi.org/10.35335/enrichment.v13i1.1302 DOI: https://doi.org/10.35335/enrichment.v13i1.1302
Thuethongchai, N., Taiphapoon, T., Chandrachai, A. y Triukose, S. (2020). Adopt big-data analytics to explore and exploit the new value for service innovation. Social Sciences, 9(3), 29. https://doi.org/10.3390/socsci9030029 DOI: https://doi.org/10.3390/socsci9030029
Tien, E. L., Ali, N. M., Miskon, S., Ahmad, N. y Abdullah, N. S. (2020). Big data analytics adoption model for Malaysian SMEs. En F. Saeed, F. Mohammed, N. Gazem (Eds.), Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing (Vol. 1073, pp. 45-53). Springer International Publishing. https://doi.org/10.1007/978-3-030-33582-3_5 DOI: https://doi.org/10.1007/978-3-030-33582-3_5
Tiwari, S., Wee, H. M. y Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330. https://doi.org/10.1016/j.cie.2017.11.017 DOI: https://doi.org/10.1016/j.cie.2017.11.017
Tong, S., Luo, X. y Xu, B. (2020). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64-78. https://acortar.link/FI7M1G DOI: https://doi.org/10.1007/s11747-019-00693-3
Tonidandel, S., King, E. B. y Cortina, J. M. (2018). Big Data Methods: Leveraging Modern Data Analytic Techniques to Build Organizational Science. Organizational Research Methods, 21(3), 525-547. https://doi.org/10.1177/1094428116677299 DOI: https://doi.org/10.1177/1094428116677299
Urbinati, A., Bogers, M., Chiesa, V. y Frattini, F. (2019). Creating and capturing value from big data: A multiple-case study analysis of provider companies. Technovation, 84-85, 21-36. https://doi.org/10.1016/j.technovation.2018.07.004 DOI: https://doi.org/10.1016/j.technovation.2018.07.004
Valdez, A., Cortes, G., Castaneda, S., Vazquez, L., Zarate, A., Salas, Y. y Atondo, G. H. (2019). Big data strategy. International Journal of Advanced Computer Science and Applications, 10(4), 285-290. https://doi.org/10.14569/ijacsa.2019.0100434 DOI: https://doi.org/10.14569/IJACSA.2019.0100434
Venkatraman, S. y Venkatraman, R. (2019). Big data security challenges and strategies. AIMS Mathematics, 4(3), 860-879. https://doi.org/10.3934/math.2019.3.860 DOI: https://doi.org/10.3934/math.2019.3.860
Wang, J. (2023). Research on big data-driven Business Management Effectiveness Enhancement Methodology. Journal of Education, Humanities and Social Sciences, 16, 277-282. https://doi.org/10.54097/ehss.v16i.9771 DOI: https://doi.org/10.54097/ehss.v16i.9771
Wang, S. C., Tsai, Y. T. y Ciou, Y. S. (2020). A hybrid big data analytical approach for analyzing customer patterns through an integrated supply chain network. Journal of Industrial Information Integration, 20, 100177. https://doi.org/10.1016/j.jii.2020.100177 DOI: https://doi.org/10.1016/j.jii.2020.100177
Yuing, T., Lizana, P. A. y Berral, F. J. (2019). Hemoglobina glicada y ejercicio: una revisión sistemática. Revista médica de Chile, 147, 480-489. https://acortar.link/ghV8BX DOI: https://doi.org/10.4067/S0034-98872019000400480
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2024 Daniel Ramírez Zarama
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Non Commercial, No Derivatives Attribution 4.0. International (CC BY-NC-ND 4.0.), that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
