تعداد نشریات | 161 |
تعداد شمارهها | 6,479 |
تعداد مقالات | 70,032 |
تعداد مشاهده مقاله | 123,008,841 |
تعداد دریافت فایل اصل مقاله | 96,239,881 |
طبقه بندی مشتریان و اولویت دهی آنها در کانون تصمیم گیری با رویکرد نظریۀ مجموعۀ راف و نظریۀ اعداد D (مطالعه موردی: تلفن همراه سونی اریکسون) | ||
مدیریت بازرگانی | ||
مقاله 10، دوره 7، شماره 1، 1394، صفحه 163-185 اصل مقاله (1.2 M) | ||
نوع مقاله: مقاله علمی پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/jibm.2015.53857 | ||
نویسندگان | ||
عفت محمدی1؛ رضا شیخ* 2 | ||
1کارشناس ارشد MBA، مهندسی صنایع و مدیریت، دانشگاه شاهرود، شاهرود، ایران | ||
2استادیار گروه مدیریت، دانشکدة مهندسی صنایع و مدیریت، دانشگاه شاهرود، شاهرود، ایران | ||
چکیده | ||
محدودیت منابع سازمان در برآوردهکردن نیاز تمامی مشتریان از یکسو و ناتوانی سازمانها در کشف اطلاعات باارزش و پنهان در دادهها از سوی دیگر سبب شده است که بسیاری از مدیران نتوانند این دادهها را به دانشی باارزش و مفید در تصمیمگیری تبدیل کنند. از اینرو بهکارگیری تکنیکی برای شناخت جزئیتر مشتریان در طبقهبندی آنها و کشف اطلاعات باارزش ناشی از خرد جمعی، بسیار حیاتی است. هدف این پژوهش، انتخاب مشتریان هدف، از بین گروههای مختلف مشتریان براساس نظر کارشناسان سازمان است. در راستای تحقق هدف پژوهش، ابتدا با استفاده از نظریۀ مجموعۀ راف، الگوهای رفتاری مشتریان شناسایی و براساس آن، مشتریان به گروههایی با ویژگیهای مشابه طبقهبندی میشوند. سپس با ایجاد توافق جمعی در نظرهای کارشناسان سازمان از طریق روش تصمیمگیری گروهی اعداد D، مشتریان هدف، بهترتیب اولویت مشخص میشوند. این پژوهش از نظر هدف، کاربردی و از نظر روششناسی، پیمایشی است که درمورد 250 نمونه از کاربران تلفن همراه سونیاریکسون اجرا شده است. نتایج پژوهش نشان میدهد با توجه به طبقهبندی مشتریان براساس اولویت، مشتریان گروه سوم از اهمیت بیشتری برخوردارند. | ||
کلیدواژهها | ||
شاخص مروجان خالص؛ نظریۀ اعداد D؛ نظریۀ مجموعۀ راف | ||
عنوان مقاله [English] | ||
Regulation and prediction of customers’ behavior according to Rough Set Theory and Selectability/Rejectability Measures (Case study: Sony Ericsson Mobile Phone) | ||
نویسندگان [English] | ||
Effat Mohammadi1؛ Reza Sheikh2 | ||
1Master in MBA, Industrial Engineering and Management, Iran | ||
2Assistant Prof., Department of Industrial Engineering and Management, University of Shahrood, Shahrood, Iran | ||
چکیده [English] | ||
Regarding the highly intensive competition in the market, nowadays using customer-oriented strategies is necessary for retention and attraction of the costumers. Nevertheless, using those kinds of strategies depends on understanding customer behavior patterns and classification of customers in accordance with those patterns. The current study aims to determine the strategies for dealing with new customers according to the natural rules dominating customers’ behavior. In order to achieve this goal (understanding customers’ behavior pattern), quickly classify the customers, and take the appropriate strategy, first the pattern ruling the behavior pattern of Sony Ericson cell phone users was suggested using NPS and RST questionnaires, and then their behaviors were predicted using Selectability/Rejectability Measures assigning them to defined classes according to RST. This study is of a practical kind regarding the purpose and is a survey from a methodology point of view. The results show that Reliability dimension is important and strategies toward new customers can be taken using current customers’ behavior. | ||
کلیدواژهها [English] | ||
net promoter score, Rough Set Theory, selectability/rejectability measures | ||
مراجع | ||
Ahn, H., Ahn, J. J., Oh, K. J. & Kim, D. H. (2011). Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques. Expert Systems with Applications, 38(5): 5005-5012. Ahn, H., Kim, K. j. & Han, I. (2007). A case-based reasoning system with the two-dimensional reduction technique for customer classification. Expert Systems with Applications, 32(4): 1011-1019. Babaei, M., younessi, E. & Bahrololoumi, S. M. (2012). Mining behavior patterns banking customers using SOM and K-Means clustering method. First National Conference on Information Technology & Networking of Payame Noor University (pnuncit). Payame Noor Tabas. Yazd. (In Persian) Berson, A., Smith, S. J. & Thearling, K. (2000). Building data mining applications for CRM, McGraw-Hill. Osborne. Chang, H. C. & Tsai, H. P. (2001). Group RFM analysis as a novel framework to discover better customer consumption behavior. Expert Systems with Applications, 38(12): 14499-14513. Chen, W. S. (2009). Analysis of a customer satisfaction survey using Rough Sets theory: A manufacturing case in Taiwan. Asia Pacific Journal of Marketing and Logistics, 21(1): 93-105. Chen, Y.-S., Cheng, C.-H., Lai, C.-J., Hsu, C.-Y., & Syu, H.-J. S. (2012). Identifying patients in target customer segments using a two-stage clustering-classification approach: A hospital-based assessment. Computers in Biology and Medicine, 42(2), 213-221. doi:http://dx.doi.org/10.1016/j.compbiomed.2011.11.010 Chiu, C. (2002). A case-based customer classification approach for direct marketing. Expert Systems with Applications, 22(2): 163-168. Dehuri, S., Patnaik, S., Ghosh, A. & Mall, R. (2008). Application of elitist multi-objective genetic algorithm for classification rule generation. Applied Soft Computing, 8(1): 477-487. Deng, X., Hu, Y. & Deng, Y. (2014). Bridge condition assessment using D numbers. The Scientific World Journal, 2014: 11. Deng, X., Hu, Y., Deng, Y. & Mahadevan, S. (2014). Supplier selection using AHP methodology extended by D numbers. Expert Systems with Applications, 41(1): 156-167. Deng, Y. (2012). D numbers: Theory and applications. Journal of Information & Computational Science, 9(9): 2421-2428. It's online at: http://www.joics.com Ghazizadeh, M., Sardari, A. & Mousavi, S. M. (2012). Identifying and ranking customers using AHP method (Branches of Bank Mellat in Tehran). http://marketingarticles.ir/ In Persian) Greco, S., Matarazzo, B. & Slowinski, R. (2007). Customer satisfaction analysis based on rough set approach. Journal of Marketing, 77(3): 325-329. Hashemi, O. (2008). Modeling of final customers choice in mobile phone by neural networks. It's online at: Digital Library of Tehran University. (In Persian) Hassanzadeh, A. R., Ghanbari, M. H. & Elahi, S. (2012). Classification of mobile banking users by data mining approach: Comparison between artificial neural networks and naïve bayes techniques. Management Research in Iran, 16(2): 57-71. It's online at: http://mri.modares.ac.ir/article-2591-664.html (In Persian) Hosseyni, S. & Ziaei Bideh, A. (2013). Using a hybrid approach based on artificial neural networks and rough set theory for modeling customers brand loyalty in mobile telecommunicating industry. Quarterly Journal of Business Management, 4(3): 43-64. (In Persian) Jiang, Y., Shang, J. & Liu, Y. (2010). Maximizing customer satisfaction through an online recommendation system: A novel associative classification model. Decision Support Systems, 48(3): 470-479. Khorshid, S., Lucas, C. & Memariani, A. (2004). A fuzzy consensus model for group decision-making: A fuzzy approach. Journal of Mnagement Studies in Development and Evolution, 41/42: 147-170. (In Persian) Kımıloğlu, H., Nasır, V. A. & Nasır, S. (2010). Discovering behavioral segments in the mobile phone market. Journal of Consumer Marketing, 24(5): 401-413. Kowsari Langari, R., Moghaddam Charkari, N. & Vahdat, D. (2014). Introducing a model for suspicious behaviors detection in electronic banking by using decision tree algorithms. Journal of Information Processing and Management, 28(3): 681-700. It's online at: http://jipm.irandoc.ac.ir/browse.php?a_code=A-10-1440-1&slc_lang=fa&sid=1 (In Persian) Lahiri, R. (2006). Comparison of data mining and statistical techniques for classification model, M.S. Thesis, Louisiana State University. Liou, J. J. & Tzeng, G. H. (2010). A dominance-based rough set approach to customer behavior in the airline market. Information Sciences, 180(11): 2230-2238. Michalski, R. S. (1983). A theory and methodology of inductive learning. Artificial Intelligence, 20(2): 111-161. Mohammadi, E. & Sheikh, R. (2012). Measure customer loyalty Using net promoter score (Case study: Nokia and Sony Ericsson mobile phones). First Iranian Mobile Congress. Sharif University of Technology-Center for Technology Studies. Tehran. (In Persian) Mohammadi, E. & Sheikh, R. (2013). Analysis of Halo Effect of customers behavior using Net promoter score (NPS) and rough set theory (RST) (Case study: Sony Ericsson Mobile Phone). Quarterly Journal of Business Management, 5(1): 119-142. (In Persian) Mohammadi, E. & Sheikh, R. (2014). Regulation and prediction of customers’ behaviors based on rough set theory and selectability/rejectability measures (Case study: Sony Ericsson Mobile Phone). Journal of Business Management, 6(1): 145-166. (In Persian) Motameni, A. R., Jafari, E. & Mojard, F. (2010). Customer Relationship Management. Commerce Printing and Publishing Company. Tehran. (In Persian) Najafi Nobar, M., Setak, M. & Fallah Tafti, A. (2011). Selecting suppliers considering features of 2nd layer suppliers by utilizing fanp procedure. International Journal of Business and Management, 6(2): 265-275. (In Persian) Ngai, E., Xiu, L. & Chau, D. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2): 2592-2602. Park, D. H., Kim, H. K., Choi, I. Y. & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39(11): 10059-10072. Salehzadeh, R. & Shahin, A. (2011). Classification of customers' needs and analyzing their behavior. New Marketing Research, 1(2): 1-16. (In Persian) Separation Sony from Ericsson (2011). It's online at: http://www.donya-e-eqtesad.com/news/423467/ (In Persian) Shaemi, A. & Barari, M. (2011). Locus of control and word of mouth communication among consumer. Journal of Business Management, 3(8): 101-114. It's online at: http://jibm.ut.ac.ir/issue_2258_2408_Volume+3%2C+Issue+8%2C+Summer+2011%2C+Page+1-172.html (In Persian) Tseng, M. L., Chiang, J. H. & Lan, L. W. (2009). Selection of optimal supplier in supply chain management strategy with analytic network process and choquet integral. Computers & Industrial Engineering, 57(1): 330-340. Turban, E., Aronson, J. E., Liang, T. P. & Sharda, R. (2007). Decision support and business intelligence systems, 8th edition, Prentice-Hall. Venouss, D. & Zohouri, B. (2011). Analyzing the value dimensions of relationship marketing and brand loyalty of mobile phones. Journal of Business Management, 3(8): 149-172. It's online at: http://jibm.ut.ac.ir/article_23932_0.html (In Persian) Wang, X.-T. & Xiong, W. (2011). An integrated linguistic-based group decision-making approach for quality function deployment. Expert Systems with Applications, 38(12): 14428-14438. Wu, C. H., Kao, S. C., Su, Y. Y. & Wu, C. C. (2005). Targeting customers via discovery knowledge for the insurance industry. Expert Systems with Applications, 29(2): 291-299. Ziarko, W. (1991). The discovery, analysis, and representation of data dependencies in databases. In Knowledge Discovery in Databases, MIT Press Cambridge, MA. USA.
| ||
آمار تعداد مشاهده مقاله: 3,043 تعداد دریافت فایل اصل مقاله: 1,549 |