Projetar o comportamento do cliente bancário nas mídias sociais por Theory Mining Method
Palavras-chave:
Customer’ behavior, Social Media, Theory Mining Method, Database, Data Mining.Resumo
Esta pesquisa qualitativa foi realizada por meio da mineração do comportamento do cliente, utilizando a teoria e o método de mineração de base e banco de dados. Por esta razão, 15 teorias de clientes foram aplicadas através do Twitter, telegramas e entrevistas com 10 gerentes de bancos da província do Curdistão. A compilação dos primeiros tópicos foi feita durante o processo de codificação e as categorias foram obtidas. Então, na etapa de codificação da base, foi determinada a ligação entre os paradigmas de codificação; Na etapa de codificação seletiva, todos os paradigmas de codificação são explicados. Comparado com pesquisas anteriores, pode-se constatar que o modelo atual elimina defeitos do modelo anterior e oferece um quadro completo de termos efetivos sobre o comportamento do cliente bancário e, finalmente, oferece um modelo de comportamento do cliente em bancos privados e governamentais.
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Referências
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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).
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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
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Samiei, R., Kouhzadi, F., MirHesami, A., & Dadi, M. A. (2018). Projetar o comportamento do cliente bancário nas mídias sociais por Theory Mining Method. Amazonia Investiga, 7(12), 203–209. Recuperado de https://amazoniainvestiga.info/index.php/amazonia/article/view/591
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