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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
| dc.contributor.author | Ching-Wen Tu | en |
| dc.contributor.author | 涂靖雯 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:36:35Z | - |
| dc.date.copyright | 2019-08-05 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-07-25 | |
| dc.identifier.citation | [1] E. E. Lawler, “Pay for performance: A motivational analysis,” In H. R. Nalbantian (Ed.), Incentives, cooperation, and risk sharing: Economic and psychological perspectives on employment contracts, 1987, pp. 69-86. Savage, MD, England: Rowman & Littlefield/Rowman and All.
[2] S. Kerr, “On the folly of rewarding A, while hoping for B,” Academy of Management Journal, 1975, 18(4), 769-783. [3] S. Singhal and M. Jena, “A study on weka tool for data preprocessing, classification and clustering,” International Journal of Innovative technology and exploring engineering (IJLtee), vol. 2, no. 6, pp. 250-253, 2013. [4] U. Gneezy, M. Stephan, and R. Pedro. 2011. “When and Why Incentives (Don't) Work to Modify Behavior,” Journal of Economic Perspectives, 25 (4): 191-210. [5] S. P. Shaprio, “Wayward capitalists: Target of the securities exchange commission.”, New Haven: Yale University Press, 1984. [6] N. S. Poser, “Chinese wall or emperor’s new clothes? Regulating conflicts of interest of securities firms in the U.S. and the U.K.,” Michigan Yearbook of International Legal Studies (Symposium: Internationalization of the Securities Markets), 1988, IX, 91-144. [7] G. Oakes, “The sales process and the paradoxes of trust,” Journal of Business Ethics, 1990, 9(8), 611-679. [8] R. Derry, “Ethics in life insurance selling,” Life Insurance Selling, January 1990, 42+ [9] N. B. Kurland, “The defense industry initiative: Ethics, accountability, and self-regulation,” Journal of Business Ethics, 1993, 12(2), 137-145. [10] J. Rapoport, S. Gelhlbach, S. Lemeshow, and D. Teres, “Resource utilization among intensive care patients. Arch Intern Med., 1992, 152 (November), 2207-2212. [11] M. Waldholz, “Doctor practice of self-referrals draws harsh criticism from medical journal,” Wall Street Journal, November 11, 1992, p. B6 [12] J. Tasalikis and D. J. Fritzsche, “Business ethics: A literature review with a focus on marketing ethics,” Journal of Business Ethics, 1989, 8, 695-743. [13] N. B. Kurland, “The ethical implications of the straight-commission compensation system — An agency perspective,” Journal of Business Ethics, 1991, 10, 767-766. [14] O. C. Walker, G. A. Churchill, and N. M. Ford, “Motivation and performance in industrial selling: Present knowledge and needed research,” Journal of Marketing Research, 1977, 14, 156-168. [15] O. C. Walker, G. A. Churchill, and N. M. Ford, “Where do we go from here? Selected conceptual and empirical issues concerning the motivation and performance of the industrial salespeople.” In G. Albaum & G. A. Churchill (Eds.), Critical issues in sales management: State-of-the-art and future research needs, College of Business Administration, University of Oregon, Eugene, 1979. [16] A. J. Dubinsky, E. N. Berkowitz, and W. Rudelius, “Ethical problems of field sales personnel,” MSU Business Topics, 1980, 28, 11-16. [17] Q. Lixia, 'Empirical research on the importance of incentive factors to life insurance agents,' 2010 International Conference On Computer Design and Applications, Qinhuangdao, 2010, pp. V5-38-V5-41. [18] P. Gingrich, “Introductory Statistics for the Social Sciences,” Department of Sociology and Social Studies, University of Regina, 2004, [Online]. Available: http://uregina.ca/~gingrich/text.htm [19] R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, “Probability & Statistics for Engineer & Scientists 9th Edition,” Pearson, 2012 [20] J. R. Quinlan, “Learning with Continuous Classes,” World Scientifics, 1992, pp. 343-348 [21] Y. Wang and H. Witten, “Inducing Model Trees for Continuous Classes,” In Proc. Of the 9th European Conf. on Machine Learning Poster Papers, 1997 [22] R. P. Santi and M. L. Khodra, “Profit Prediction using Regression Model for Travel Agents,” International Workshop on Big Data and Information Security (IWBIS), 2018, May, pp. 7-12 [23] P. A. Castillo, A. M. Mora, H. Faris, J. Merelo, and P. Garcia-Sanches, A. J. Fernandes-Ares, P. D. L. Cuevas and M. I. Garcia-Arenas, “Applying Computational Intelligence Methods for Predicting The Sales of Newly Published Books in Real Editorial Business Management Environment,” Knowledge-Based Systems, 2017, January, pp. 133-151. [24] I. Maryani and D. Riana, “Clustering and profiling of customers using RFM for customer relationship management recommendations,” Proceedings of International Conference on Cyber and IT Service Management (CITSM), 2017, pp. 1-6. [25] M. Tsoy, V. Shchekoldin, “RFM-analysis as a tool for segmentation of high-tech product’s consumers,” Proceedings of International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering(APEIE), 2017, pp. 290-293. [26] J. Wei, S. Lin, Y. Yang, and H. Wu, “Applying data mining and RFM model to analyze customer’s values of a veterinary hospital,” Proceedings of International Symposium of Computer, Consumer and Control (IS3C), 2016, pp. 481-484. [27] R. Daoud, A. Amine and B. Bouikhalene, “Combining RFM model and clustering techniques for customer value analysis of a company selling online,” Proceedings of International Conference of Computer Systems and Applications (AICCSA), 2015, pp. 1-6 [28] C. Doi, M. Katagiri, T. Araki, D. Ikeda, and H. Shigeno, “Is he Becoming an Excellent Customer for us? A Customer Level Prediction Method for a Customer Relationship Management System,” 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), Krakow, 2018, pp. 320-326. [29] Z. Huang, “Clustering large data sets with mixed numeric and categorical values,” In The First Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1997, pp. 21-34. [30] K. Pearson, “On lines and planes of closest fit to systems of points in space,” Philosophical Magazine, 1901, 2, pp. 559–572. [31] H. Hotelling, “Analysis of a complex of statistical variables into principal components,” Journal of Educational Psychology, 1933, 24(6), pp. 417-441. [32] J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281-297. [33] M. R. Anderberg, “Cluster Analysis for Applications,” Academic Press, 1973. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21519 | - |
| dc.description.abstract | 大部分的人壽保險公司都會實施獎勵制度來刺激員工的表現，然而要計算這些獎勵制度帶來的實際影響並不容易，也很難透過這些制度來預估每年實際的收入，因此保險公司時常面臨預估的目標與最後實際的收入有所差異，保險公司希望能夠了解每個獎勵制度所能帶來的收益有多少。
在本篇論文中，我們先透過資料視覺化 (Data Visualization) 的方式來了解公司的整體營運狀況，接下來我們利用分群演算法 (Clustering Algorithm) 來將保單以及產品的貢獻度進行分類，在特定的保險類型之產品對公司的整體收益有比較高的貢獻度。除了分析產品對公司的貢獻之外，我們也使用分群演算法來將每個業務員每年的貢獻進行分類，不同組的業務員的貢獻有不小的差距，因此，我們觀察到業務員在每年的表現中是有存在不同的差異。 利用上述的方法對公司有基本的了解之後，我們分析長期在公司工作的業務員，可以透過這些業務員來了解他們長期的發展跟公司之間的關係，經過分群演算法的計算後，大致上可以將這些業務員分成兩類，貢獻度較高的業務員人數大概佔所有業務員人數的百分之二十，而這些業務員的業績大概佔整間公司的業績之百分之八十，當我們將這相同的模型套用到每一年所有的業務員上，我們找到了相同的結論。 除了有八十二十法則存在於這間公司，我們也觀察到這間公司的產品與獎勵制度間的關聯非常重要，如果公司發行的產品符合市場需求並且有提出吸引業務員的獎勵制度，他們的收入可以在短時間內提高許多，如果在未來公司能夠符合上述的兩個條件，可以幫助他們的業績在短時間內迅速成長。 | zh_TW |
| dc.description.abstract | Implementing the incentive program is very common in the life insurance company. However, the real influence of these incentive systems cannot be easily calculated, and the organization suffers that it is unable to predict the actual income annually. The company usually faces great gaps between its target and the real operation with the incentive programs. The company wants to know the contribution of each incentive program when they implemented.
In our thesis, we use the visualization method to understand the overall trend of the company. After that, we use clustering methods to separate the performance of insurance policies and products. We discover that some of the insurance types of products have a higher contribution. We also use clustering methods to separate the performance of each agent and we discover that each group of performance have gaps between them. We realize that the contribution between agents has great diversity. Afterward, we analysis the agents who have worked in the company for a long time to discover the relationship between agents and the company. We observe that the agents can generally be divided into two groups and those who in the top group is around twenty percent of agents in the company and their contribution to the company is near eighty percent. When we fit the same model back to all the agents in every year, we have the same conclusion. In addition to discovering the rule of 80-20 exists in the company, we observe that the company can improve their income if they launch the products meet the market requirement and provide the incentive programs that attract agents. In the future, the company can spur its performance growth rapidly when they meet the two factors that we mentioned above. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:36:35Z (GMT). No. of bitstreams: 1 ntu-108-R06725011-1.pdf: 4070716 bytes, checksum: aa344c1ffe543eae21e6f642648c6e51 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES vii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Objectives 2 1.3 Thesis object and scope 3 1.4 Thesis Outline 3 Chapter 2 Background and Related Work 5 2.1 Behavior under the Incentive Program 5 2.1.1 Dysfunctional Outcome 6 2.1.2 Important of Incentive Factors 7 2.2 Performance Prediction 7 2.2.1 Regression Model 8 2.2.2 RFM Model 9 Chapter 3 Method 11 3.1 Dataset 11 3.2 Process 15 3.3 Data Preparation 16 3.4 Data Visualization 20 3.5 Data Grouping 21 3.5.1 Product Clustering 21 3.5.2 Agent Clustering 22 3.6 Data Analysis 22 3.6.1 5-Year Working Agent Clustering 23 3.6.2 Each Year Agent Clustering 24 Chapter 4 Experiment and Result 25 4.1 General Data Visualization 25 4.2 Clustering by Product 28 4.3 Clustering by Agent 32 4.4 5-Year Working Agent Clustering 37 4.5 Each Year Agent Clustering 44 4.6 Top 20 Percent Agents’ Performance 48 Chapter 5 Conclusion and Future Work 51 5.1 Conclusion 51 5.2 Recommendation 52 5.3 Future Work 53 REFERENCE 55 Appendix A. PCA Result of Five-Year Agents 59 Appendix B. PCA Result of Each Year Agent 62 | |
| dc.language.iso | en | |
| dc.title | 壽險業務員績效與業績目標關係之分析研究 | zh_TW |
| dc.title | On the Relationship of Insurance Agents Performance and Sales Target | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 吳玲玲(Ling-Ling Wu) | |
| dc.contributor.oralexamcommittee | 周子元(Tzy-Yuan Chou) | |
| dc.subject.keyword | 資料探勘,分群演算法,獎勵制度,人壽保險公司,資料視覺化,80-20 法則, | zh_TW |
| dc.subject.keyword | Data Mining,Clustering Algorithm,Incentive Program,Life Insurance Company,Data Visualization,Rule of 80-20, | en |
| dc.relation.page | 65 | |
| dc.identifier.doi | 10.6342/NTU201901717 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-07-25 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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