Design banking customer’ behavior in Social Media by Theory Mining Method
Keywords:
Customer’ behavior, Social Media, Theory Mining Method, Database, Data Mining.
Abstract
This qualitative research was conducted by mining the client's behavior by using the theory and method of base mining and database. For this reason, 15 customer theories were applied through Twitter, telegrams and interviews with 10 bank managers from the province of Kurdistan. The compilation of the first topics was done during the coding process and the categories were obtained. Then, in the coding step of the base, the link between the coding paradigms was determined; In the step of selective coding, all the coding paradigms are explained. Compared with previous research, it can be found that the current model eliminates defects of the previous model and offers a complete picture of effective terms on the behavior of the banking client and, finally, offers a model of customer behavior in private and government banks
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References
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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.
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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
Published
2018-02-27
How to Cite
Samiei, R., Kouhzadi, F., MirHesami, A., & Dadi, M. (2018). Design banking customer’ behavior in Social Media by Theory Mining Method. Amazonia Investiga, 7(12), 203-209. Retrieved from https://amazoniainvestiga.info/index.php/amazonia/article/view/591
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