Factors affecting the big data adoption as a marketing tool in SMEs

The change brought by Big Data about the way to analyze the data is revolutionary. The technology related to Big Data supposes a before and after in the form of obtaining valuable information for the companies since it allows to manage a large volume of data, practically in real time and obtain a gr...

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Autores:
Viloria Silva, Amelec Jesus
Hernández-Fernández, Lissette
Torres Cuadrado, Esperanza Margarita
Mercado Caruso, Nohora Nubia
Rengifo Espinosa, Carlos
Acosta Ortega, Felipe
Hernández P, Hugo
Jimenez Delgado, Genett Isabel
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5228
Acceso en línea:
https://hdl.handle.net/11323/5228
https://repositorio.cuc.edu.co/
Palabra clave:
Big data
Intention to use
UTAUT
Acceptance of technologies
Resistance to use
Partial least squares
Rights
openAccess
License
Attribution-NonCommercial-ShareAlike 4.0 International
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network_acronym_str RCUC2
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repository_id_str
dc.title.spa.fl_str_mv Factors affecting the big data adoption as a marketing tool in SMEs
title Factors affecting the big data adoption as a marketing tool in SMEs
spellingShingle Factors affecting the big data adoption as a marketing tool in SMEs
Big data
Intention to use
UTAUT
Acceptance of technologies
Resistance to use
Partial least squares
title_short Factors affecting the big data adoption as a marketing tool in SMEs
title_full Factors affecting the big data adoption as a marketing tool in SMEs
title_fullStr Factors affecting the big data adoption as a marketing tool in SMEs
title_full_unstemmed Factors affecting the big data adoption as a marketing tool in SMEs
title_sort Factors affecting the big data adoption as a marketing tool in SMEs
dc.creator.fl_str_mv Viloria Silva, Amelec Jesus
Hernández-Fernández, Lissette
Torres Cuadrado, Esperanza Margarita
Mercado Caruso, Nohora Nubia
Rengifo Espinosa, Carlos
Acosta Ortega, Felipe
Hernández P, Hugo
Jimenez Delgado, Genett Isabel
dc.contributor.author.spa.fl_str_mv Viloria Silva, Amelec Jesus
Hernández-Fernández, Lissette
Torres Cuadrado, Esperanza Margarita
Mercado Caruso, Nohora Nubia
Rengifo Espinosa, Carlos
Acosta Ortega, Felipe
Hernández P, Hugo
Jimenez Delgado, Genett Isabel
dc.subject.spa.fl_str_mv Big data
Intention to use
UTAUT
Acceptance of technologies
Resistance to use
Partial least squares
topic Big data
Intention to use
UTAUT
Acceptance of technologies
Resistance to use
Partial least squares
description The change brought by Big Data about the way to analyze the data is revolutionary. The technology related to Big Data supposes a before and after in the form of obtaining valuable information for the companies since it allows to manage a large volume of data, practically in real time and obtain a great volume of information that gives companies great competitive advantages. The objective of this work is evaluating the factors that affect the acceptance of this new technology by small and medium enterprises. To that end, the technology acceptance model called Unified Theory of Technology Adoption and Use of Technology (UTAUT) was adapted to the Big Data context to which an inhibitor was added: resistance to the use of new technologies. The structural model was assessed using Partial Least Squares (PLS) with an adequate global adjustment. Among the results, it stands out that a good infrastructure is more relevant for the use of Big Data than the difficulty of its use, accepting that it is necessary to make an effort in its implementation.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-08-31T03:10:25Z
dc.date.available.none.fl_str_mv 2019-08-31T03:10:25Z
dc.date.issued.none.fl_str_mv 2019-07-26
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
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identifier_str_mv 1865-0929
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REDICUC - Repositorio CUC
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dc.language.iso.none.fl_str_mv eng
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dc.relation.references.spa.fl_str_mv 1. Amelec, V.: Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv. Sci. Lett. 21(5), 1406–1408 (2015) 2. Varela, I.N., Cabrera, H.R., Lopez, C.G., Viloria, A., Gaitán, A.M., Henry, M.A.: Methodology for the reduction and integration of data in the performance measurement of industries cement plants. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 33–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_4 3. Lis-Gutiérrez, M., Gaitán-Angulo, M., Balaguera, M.I., Viloria, A., Santander-Abril, J.E.: Use of the industrial property system for new creations in colombia: a departmental analysis (2000–2016). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. Lecture Notes in Computer Science, vol. 10943, pp. 786–796. Springer, Cham (2018). https://doi.org/10. 1007/978-3-319-93803-5_74 4. Anuradha, K., Kumar, K.A.: An E-commerce application for presuming missing items. Int. J. Comput. Trends Technol. 4, 2636–2640 (2013) 5. Larose, D.T., Larose, C.D.: Discovering Knowledge in Data (2014). https://doi.org/10.1002/ 9781118874059 6. Pickrahn, I., et al.: Contamination incidents in the pre-analytical phase of forensic DNA analysis in Austria—Statistics of 17 years. Forensic Sci. Int. Genet. 31, 12–18 (2017). https://doi.org/10.1016/j.fsigen.2017.07.012 7. Barrios-Hernández, K.D.C., Contreras-Salinas, J.A., Olivero-Vega, E.: La Gestión por Procesos en las Pymes de Barranquilla: Factor Diferenciador de la Competitividad Organizacional. Información tecnológica 30(2), 103–114 (2019) 8. Prajapati, D.J., Garg, S., Chauhan, N.C.: Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment. Futur. Comput. Inform. J. 2, 19–30 (2017). https://doi.org/10.1016/j.fcij.2017.04.003 9. Abdullah, M., Al-Hagery, H.: Classifiers’ accuracy based on breast cancer medical data and data mining techniques. Int. J. Adv. Biotechnol. Res. 7, 976–2612 (2016) 10. Khanali, H.: A survey on improved algorithms for mining association rules. Int. J. Comput. Appl. 165, 8887 (2017) 11. Ban, T., Eto, M., Guo, S., Inoue, D., Nakao, K., Huang, R.: A study on association rule mining of darknet big data. In: International Joint Conference on Neural Networks, pp. 1–7 (2015). https://doi.org/10.1109/IJCNN.2015.7280818 12. Vo, B., Le, B.: Fast algorithm for mining generalized association rules. Int. J. Database Theory Appl. 2, 1–12 (2009) 13. Al-hagery, M.A.: Knowledge discovery in the data sets of hepatitis disease for diagnosis and prediction to support and serve community. Int. J. Comput. Electron. Res. 4, 118–125 (2015) 14. Amelec, V., Alexander, P.: Improvements in the automatic distribution process of finished product for pet food category in multinational company. Adv. Sci. Lett. 21(5), 1419–1421 (2015) 15. Cabarcas, J., Paternina, C.: Aplicación del análisis discriminante para identificar diferencias en el perfil productivo de las empresas exportadoras y no exportadoras del Departamento del Atlántico de Colombia. Revista Ingeniare 6(10), 33–48 (2011) 16. Caridad, J.M., Ceular, N.: Un análisis del mercado de la vivienda a través de redes neuronales artificiales. Estudios de economía aplicada (18), 67–81 (2001) 17. Correia, A., Barandas, H., Pires, P.: Applying artificial neural networks to evaluate export performance: a relational approach. Rev. Onternational Comp. Manag. 10(4), 713–734 (2009) 18. De La Hoz, E., González, Á., Santana, A.: Metodología de Medición del Potencial Exportador de las Organizaciones Empresariales. Información Tecnológica 27(6), 11–18 (2016) 19. De La Hoz, E., López, P.: Aplicación de Técnicas de Análisis de Conglomerados y Redes Neuronales Artificiales en la Evaluación del Potencial Exportador de una Empresa. Información Tecnológica 28(4), 67–74 (2017) 20. Escandón, D., Hurtado, A.: Los determinantes de la orientación exportadora y los resultados en las pymes exportadoras en Colombia. Estudios Gerenciales 30(133), 430–440 (2014) 21. Kumar, G., Malik, H.: Generalized regression neural network based wind speed prediction model for western region of India. Procedia Comput. Sci. 93(September), 26–32 (2016). https://doi.org/10.1016/j.procs.07.177 22. Obschatko, E., Blaio, M.: El perfil exportador del sector agroalimentario argentino. Las profucciones de alto valor. Estudio 1. EG.33.7. Ministerio de Economía de Argentina (2003) 23. Olmedo, E., Velasco, F., Valderas, J.M.: Caracterización no lineal y predicción no paramétrica en el IBEX35. Estudios de Economía Aplicada 25(3), 1–3 (2007) 24. Paredes, D.: Elaboración del plan de negocios de exportación. Programa de Plan de Negocio, Exportador- PLANEX (2016). https://goo.gl/oTnARL 25. Qazi, N.: Effectof Feature Selection, Synthetic Minority Over-sampling (SMOTE) And Under-sampling on Class imbalance Classification (2012). https://doi.org/10.1109/UKSim. 116 26. Smith, D.: A neural network classification of export success in Japanese service firms. Serv. Mark. Q. 26(4), 95–108 (2005) 27. Sharmila, S., Kumar, M.: An optimized farthest first clustering algorithm. In: Nirma University International Conference on Engineering, NUiCONE 2013, pp. 1–5 (2013). https://doi.org/10.1109/NUiCONE.2013.6780070 28. Sun, G., Hoff, S., Zelle, B., Nelson, M.: Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM 10 Concentrations and Emissions from Swine Buildings, vol. 0300, no. 08 (2008) 29. Uberbacher, E.C., Mural, R.J.: Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach. Proc. Natl. Acad. Sci. 88(24), 11261–11265 (1991)
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spelling Viloria Silva, Amelec JesusHernández-Fernández, LissetteTorres Cuadrado, Esperanza MargaritaMercado Caruso, Nohora NubiaRengifo Espinosa, CarlosAcosta Ortega, FelipeHernández P, HugoJimenez Delgado, Genett Isabel2019-08-31T03:10:25Z2019-08-31T03:10:25Z2019-07-261865-0929https://hdl.handle.net/11323/5228Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The change brought by Big Data about the way to analyze the data is revolutionary. The technology related to Big Data supposes a before and after in the form of obtaining valuable information for the companies since it allows to manage a large volume of data, practically in real time and obtain a great volume of information that gives companies great competitive advantages. The objective of this work is evaluating the factors that affect the acceptance of this new technology by small and medium enterprises. To that end, the technology acceptance model called Unified Theory of Technology Adoption and Use of Technology (UTAUT) was adapted to the Big Data context to which an inhibitor was added: resistance to the use of new technologies. The structural model was assessed using Partial Least Squares (PLS) with an adequate global adjustment. Among the results, it stands out that a good infrastructure is more relevant for the use of Big Data than the difficulty of its use, accepting that it is necessary to make an effort in its implementation.Universidad Peruana de Ciencias Aplicadas, Universidad de la Costa, Fundación Universitaria Popayán, Corporación Universitaria Latinoamericana, Corporación Universitaria Reformada.Viloria Silva, Amelec JesusHernández-Fernández, LissetteTorres Cuadrado, Esperanza MargaritaMercado Caruso, Nohora NubiaRengifo Espinosa, CarlosAcosta Ortega, FelipeHernández P, HugoJimenez Delgado, Genett IsabelengCommunications in Computer and Information Sciencehttps://doi.org/10.1007/978-981-32-9563-6_41. Amelec, V.: Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv. Sci. Lett. 21(5), 1406–1408 (2015) 2. Varela, I.N., Cabrera, H.R., Lopez, C.G., Viloria, A., Gaitán, A.M., Henry, M.A.: Methodology for the reduction and integration of data in the performance measurement of industries cement plants. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 33–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_4 3. Lis-Gutiérrez, M., Gaitán-Angulo, M., Balaguera, M.I., Viloria, A., Santander-Abril, J.E.: Use of the industrial property system for new creations in colombia: a departmental analysis (2000–2016). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. Lecture Notes in Computer Science, vol. 10943, pp. 786–796. Springer, Cham (2018). https://doi.org/10. 1007/978-3-319-93803-5_74 4. Anuradha, K., Kumar, K.A.: An E-commerce application for presuming missing items. Int. J. Comput. Trends Technol. 4, 2636–2640 (2013) 5. Larose, D.T., Larose, C.D.: Discovering Knowledge in Data (2014). https://doi.org/10.1002/ 9781118874059 6. Pickrahn, I., et al.: Contamination incidents in the pre-analytical phase of forensic DNA analysis in Austria—Statistics of 17 years. Forensic Sci. Int. Genet. 31, 12–18 (2017). https://doi.org/10.1016/j.fsigen.2017.07.012 7. Barrios-Hernández, K.D.C., Contreras-Salinas, J.A., Olivero-Vega, E.: La Gestión por Procesos en las Pymes de Barranquilla: Factor Diferenciador de la Competitividad Organizacional. Información tecnológica 30(2), 103–114 (2019) 8. Prajapati, D.J., Garg, S., Chauhan, N.C.: Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment. Futur. Comput. Inform. J. 2, 19–30 (2017). https://doi.org/10.1016/j.fcij.2017.04.003 9. Abdullah, M., Al-Hagery, H.: Classifiers’ accuracy based on breast cancer medical data and data mining techniques. Int. J. Adv. Biotechnol. Res. 7, 976–2612 (2016) 10. Khanali, H.: A survey on improved algorithms for mining association rules. Int. J. Comput. Appl. 165, 8887 (2017) 11. Ban, T., Eto, M., Guo, S., Inoue, D., Nakao, K., Huang, R.: A study on association rule mining of darknet big data. In: International Joint Conference on Neural Networks, pp. 1–7 (2015). https://doi.org/10.1109/IJCNN.2015.7280818 12. Vo, B., Le, B.: Fast algorithm for mining generalized association rules. Int. J. Database Theory Appl. 2, 1–12 (2009) 13. Al-hagery, M.A.: Knowledge discovery in the data sets of hepatitis disease for diagnosis and prediction to support and serve community. Int. J. Comput. Electron. Res. 4, 118–125 (2015) 14. Amelec, V., Alexander, P.: Improvements in the automatic distribution process of finished product for pet food category in multinational company. Adv. Sci. Lett. 21(5), 1419–1421 (2015) 15. Cabarcas, J., Paternina, C.: Aplicación del análisis discriminante para identificar diferencias en el perfil productivo de las empresas exportadoras y no exportadoras del Departamento del Atlántico de Colombia. Revista Ingeniare 6(10), 33–48 (2011) 16. Caridad, J.M., Ceular, N.: Un análisis del mercado de la vivienda a través de redes neuronales artificiales. Estudios de economía aplicada (18), 67–81 (2001) 17. Correia, A., Barandas, H., Pires, P.: Applying artificial neural networks to evaluate export performance: a relational approach. Rev. Onternational Comp. Manag. 10(4), 713–734 (2009) 18. De La Hoz, E., González, Á., Santana, A.: Metodología de Medición del Potencial Exportador de las Organizaciones Empresariales. Información Tecnológica 27(6), 11–18 (2016) 19. De La Hoz, E., López, P.: Aplicación de Técnicas de Análisis de Conglomerados y Redes Neuronales Artificiales en la Evaluación del Potencial Exportador de una Empresa. Información Tecnológica 28(4), 67–74 (2017) 20. Escandón, D., Hurtado, A.: Los determinantes de la orientación exportadora y los resultados en las pymes exportadoras en Colombia. Estudios Gerenciales 30(133), 430–440 (2014) 21. Kumar, G., Malik, H.: Generalized regression neural network based wind speed prediction model for western region of India. Procedia Comput. Sci. 93(September), 26–32 (2016). https://doi.org/10.1016/j.procs.07.177 22. Obschatko, E., Blaio, M.: El perfil exportador del sector agroalimentario argentino. Las profucciones de alto valor. Estudio 1. EG.33.7. Ministerio de Economía de Argentina (2003) 23. Olmedo, E., Velasco, F., Valderas, J.M.: Caracterización no lineal y predicción no paramétrica en el IBEX35. Estudios de Economía Aplicada 25(3), 1–3 (2007) 24. Paredes, D.: Elaboración del plan de negocios de exportación. Programa de Plan de Negocio, Exportador- PLANEX (2016). https://goo.gl/oTnARL 25. Qazi, N.: Effectof Feature Selection, Synthetic Minority Over-sampling (SMOTE) And Under-sampling on Class imbalance Classification (2012). https://doi.org/10.1109/UKSim. 116 26. Smith, D.: A neural network classification of export success in Japanese service firms. Serv. Mark. Q. 26(4), 95–108 (2005) 27. Sharmila, S., Kumar, M.: An optimized farthest first clustering algorithm. In: Nirma University International Conference on Engineering, NUiCONE 2013, pp. 1–5 (2013). https://doi.org/10.1109/NUiCONE.2013.6780070 28. Sun, G., Hoff, S., Zelle, B., Nelson, M.: Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM 10 Concentrations and Emissions from Swine Buildings, vol. 0300, no. 08 (2008) 29. Uberbacher, E.C., Mural, R.J.: Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach. Proc. Natl. Acad. 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