Diseño de la banca de clientes en el comportamiento en los medios de comunicación social
Palabras clave:
comportamiento del cliente, redes sociales, método de extracción de teoría, base de datos, minería de datosResumen
Esta investigación cualitativa se realizó mediante la minería del comportamiento del cliente mediante el uso de la teoría y el método de minería de base y base de datos. Por esta razón, se aplicaron 15 teorías del cliente a través de Twitter, telegramas y entrevistas con 10 gerentes de bancos de la provincia de Kurdistán. La recopilación de los primeros temas se realizó durante el proceso de codificación y se obtuvieron las categorías. Luego, en el paso de codificación de la base, se determinó el vínculo entre los paradigmas de codificación; en el paso de codificación selectiva, se explican todos los paradigmas de codificación. En comparación con investigaciones anteriores, se puede encontrar que el modelo actual elimina defectos del modelo anterior y ofrece una imagen completa de términos efectivos sobre el comportamiento del cliente bancario y, finalmente, ofrece un modelo de comportamiento del cliente en bancos privados y gubernamentales.
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Abbasolu, M. A., Gedik, B., and Ferhatosmanolu, H. "Aggregate profile clustering for telco analytics,"Proceedings of the VLDB Endowment (6:12) 2013, pp 1234-1237.
And emotions on behavioral intentions”, Journal of Services Marketing, 22 (4), pp. 303- 315.
Andrews, M., Luo, X., Fang, Z. and Ghose, A. (2015), “Mobile ad effectiveness: hyper- contextual targeting with crowdedness”, Marketing Science
Baker. A, Ricciardi.k The Psychology of Financial Planning and Investing.wiley. p23
Bayus, B.L. (2013), “Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm community”, Management Science, Vol. 59 No. 1, pp. 226-244.
Bigne, J. E., Mattila, A. S. & Andreu, L. (2008). “The impact of experiential consumption cognitions
Blackwell, R.D., Miniard, P.W. and Engel, J.F. (2005), Consumer Behavior, 10th ed., South- Western College Publications.
Chan, K.W., Li, S.Y. and Zhu, J.J. (2015), “Fostering customer ideation in crowdsourcing community: the role of peer-to-peer and peer- to-firm interactions”, Journal of Interactive Marketing, Vol. 31, pp. 42-62. Creswell, J. W. & Miller, D. L. (2000). Determining Validity in Qualitative Inquiry,Theory into Practice, 39(3): 124-131.
De Valck, K. (2007). The war of the eTribes: Online conflicts and communal consumption. In B. Cova, R. Kozinets, & A. Shankar (Eds.), Consumer tribes (pp. 260_274). Oxford: Butterworth-Heinemann
Erevelles, S., Fukawa, N. and Swayne, L. (2015), “Bid data consumer analytics and the transformation of marketing”,
Goulding, C. (2005). Grounded theory, ethnography and phenomenology: A comparative analysis of three qualitative strategies for marketing research. European Journal of Marketing, 39(3), 294_308.
Handelman, J. (1998). Ensouling consumption: A netnographic exploration of the meaning of boycotting behavior. Advances in Consumer Research, 25(1),475_480.
King, R.A., Racherla, P. Bush, V.D. (2014), “What we know and don’t know about online word-of-mouth: a review and synthesis of the literature”, Journal of Interactive Marketing, Vol. 28 No. 3, pp. 167-183
Klapdor, S., Anderl, E.A., von Wangenheim, F. and Schumann, J.H. (2014), “Finding the right words: the influence of keyword characteristics on performance of paid search campaigns”, Journal of Interactive Marketing, Vol. 28 No. 4, pp. 285-301.
Knight, Frank H. 1921. Risk, Uncertainty, and Profit. Boston, MA: Houghton Mifflin.
Knudsen and Kjeldgaard (2014), ONLINE RECEPTION ANALYSIS: BIG DATA IN QUALITATIVE MARKETING RESEARCH, Research in Consumer Behavior, Volume 16, 217_242
Kozinets, R. V. (2009). Netnography: Doing ethnographic research online. Los Angeles, CA:Sage.
Malthouse, E.C. (2007), “Mining for trigger events with survival analysis”, Data Mining and Knowledge Discovery, Vol. 15 No. 3, pp. 383- 402.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H.(2011).
Big data: The next frontier for innovation, competition, and productivity. Sydney: McKinsey Global Institute.
Mayer-Scho¨ nberger, V., & Cukier, K. (2013). Big data. Boston, MA: Houghton Mifflin Harcourt.
Muniz, A. M., Jr., & O’Guinn, T. C. (2001). Brand community. Journal of Consumer Research, 27(4), 412_432.
Murray, K.B. and Häubl, G. (2009), “Personalization without interrogation: towards more effective interactions between
Ngai, E. W., Xiu, L., and Chau, D. C. (2009)"Application of data mining techniques in customer relationship management: A literature review and classification," Expert systems with applications (36:2) pp 2592-2602.
Normandeau, K. (2013), “Beyond volume, variety and velocity is the issue of big data veracity”, Inside BigData, available at: http://insidebigdata.com/2013/09/12/beyondvol ume- variety-velocity-issue-big-data-veracity/ (accessed 15 April 2015).
Rokka, J., & Moisander, J. (2009). Environmental dialogue in online communities: Negotiating ecological citizenship among global travelers. International Journal of Consumer Studies, 33(2), 199_205. doi:10.1111/j.1470- 6431.2009.00759.x
And emotions on behavioral intentions”, Journal of Services Marketing, 22 (4), pp. 303- 315.
Andrews, M., Luo, X., Fang, Z. and Ghose, A. (2015), “Mobile ad effectiveness: hyper- contextual targeting with crowdedness”, Marketing Science
Baker. A, Ricciardi.k The Psychology of Financial Planning and Investing.wiley. p23
Bayus, B.L. (2013), “Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm community”, Management Science, Vol. 59 No. 1, pp. 226-244.
Bigne, J. E., Mattila, A. S. & Andreu, L. (2008). “The impact of experiential consumption cognitions
Blackwell, R.D., Miniard, P.W. and Engel, J.F. (2005), Consumer Behavior, 10th ed., South- Western College Publications.
Chan, K.W., Li, S.Y. and Zhu, J.J. (2015), “Fostering customer ideation in crowdsourcing community: the role of peer-to-peer and peer- to-firm interactions”, Journal of Interactive Marketing, Vol. 31, pp. 42-62. Creswell, J. W. & Miller, D. L. (2000). Determining Validity in Qualitative Inquiry,Theory into Practice, 39(3): 124-131.
De Valck, K. (2007). The war of the eTribes: Online conflicts and communal consumption. In B. Cova, R. Kozinets, & A. Shankar (Eds.), Consumer tribes (pp. 260_274). Oxford: Butterworth-Heinemann
Erevelles, S., Fukawa, N. and Swayne, L. (2015), “Bid data consumer analytics and the transformation of marketing”,
Goulding, C. (2005). Grounded theory, ethnography and phenomenology: A comparative analysis of three qualitative strategies for marketing research. European Journal of Marketing, 39(3), 294_308.
Handelman, J. (1998). Ensouling consumption: A netnographic exploration of the meaning of boycotting behavior. Advances in Consumer Research, 25(1),475_480.
King, R.A., Racherla, P. Bush, V.D. (2014), “What we know and don’t know about online word-of-mouth: a review and synthesis of the literature”, Journal of Interactive Marketing, Vol. 28 No. 3, pp. 167-183
Klapdor, S., Anderl, E.A., von Wangenheim, F. and Schumann, J.H. (2014), “Finding the right words: the influence of keyword characteristics on performance of paid search campaigns”, Journal of Interactive Marketing, Vol. 28 No. 4, pp. 285-301.
Knight, Frank H. 1921. Risk, Uncertainty, and Profit. Boston, MA: Houghton Mifflin.
Knudsen and Kjeldgaard (2014), ONLINE RECEPTION ANALYSIS: BIG DATA IN QUALITATIVE MARKETING RESEARCH, Research in Consumer Behavior, Volume 16, 217_242
Kozinets, R. V. (2009). Netnography: Doing ethnographic research online. Los Angeles, CA:Sage.
Malthouse, E.C. (2007), “Mining for trigger events with survival analysis”, Data Mining and Knowledge Discovery, Vol. 15 No. 3, pp. 383- 402.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H.(2011).
Big data: The next frontier for innovation, competition, and productivity. Sydney: McKinsey Global Institute.
Mayer-Scho¨ nberger, V., & Cukier, K. (2013). Big data. Boston, MA: Houghton Mifflin Harcourt.
Muniz, A. M., Jr., & O’Guinn, T. C. (2001). Brand community. Journal of Consumer Research, 27(4), 412_432.
Murray, K.B. and Häubl, G. (2009), “Personalization without interrogation: towards more effective interactions between
Ngai, E. W., Xiu, L., and Chau, D. C. (2009)"Application of data mining techniques in customer relationship management: A literature review and classification," Expert systems with applications (36:2) pp 2592-2602.
Normandeau, K. (2013), “Beyond volume, variety and velocity is the issue of big data veracity”, Inside BigData, available at: http://insidebigdata.com/2013/09/12/beyondvol ume- variety-velocity-issue-big-data-veracity/ (accessed 15 April 2015).
Rokka, J., & Moisander, J. (2009). Environmental dialogue in online communities: Negotiating ecological citizenship among global travelers. International Journal of Consumer Studies, 33(2), 199_205. doi:10.1111/j.1470- 6431.2009.00759.x
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Publicado
2018-02-27
Cómo citar
Samiei, R., Kouhzadi, F., MirHesami, A., & Dadi, M. A. (2018). Diseño de la banca de clientes en el comportamiento en los medios de comunicación social. Amazonia Investiga, 7(12), 203–209. Recuperado a partir de https://amazoniainvestiga.info/index.php/amazonia/article/view/591
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