對人壽保險業而言，隨著資訊技術環境成熟、網際網路使用人口的增加、顧客需求改變等影響下，人壽保險公司紛紛計畫應用網際網路的力量，創造新的經營模式，以增加顧客購買意向與提高顧客忠誠度。而隨著網際網路的盛行，電子郵件也因為「快速」、「有效」、「低成本」被大量使用做為行銷的管道之一。而推薦系統是網路上常運用的技術，主要用來提高顧客的購買意向，我們將此技術應用在保險業上，希望藉由個人化保險推薦系統推薦給顧客其有興趣及需要的保險商品及資訊，進而增加顧客對於保險商品的購買慾望跟提高顧客對保險公司的忠誠度。此外，沉浸理論近年來廣泛地被用在網路研究上，它是一種暫時性的、主觀之經驗，並且可用來解釋人們為什麼願意繼續再從事某種活動之原因，在本研究中欲透過沉浸理論來協助檢視其保險個人化推薦之有效性。
根據上述幾項論述，本研究採用資料探勘技術去建立ㄧ套個人化保險電子郵件推薦系統並且利用網路問卷協助探討個人化保險電子郵件推薦是否能夠在顧客瀏覽個人化保險推薦時產生沉浸效果，而透過沉浸效果的產生是否能夠影響其保險推薦的施行效果。
本研究依據所欲研究的問題，針對台灣地區20歲以上有使用電子郵件民眾為研究對象，採用雙重取樣法回收最終有效網路問卷130份，回收率為78.78 %。而研究方法包括次數分配(Frequency Distribution)、因素分析(Factor Analysis)、Cronbach’s α、結構方程式模型(Structural Equation Model)。本研究得出的結果如下：
(1) 透過資料探勘技術建立的保險個人化推薦系統能夠依據保險公司資料庫預測新顧客適合的保險推薦險種。
(2) 當顧客透過電子郵件瀏覽保險公司所提供的個人化保險推薦能夠產生沉浸效果。
(3) 顧客瀏覽個人化保險電子郵件推薦所產生的沉浸經驗對保險推薦效果有其直接的影響。

With the stability of information technology environment, the growth of Internet population and changes in customer needs have prompted life insurance companies to adopt their business models to grasp opportunities in the Internet area, and to increase more customers’ purchase intention and loyalty. E- ail that has the characteristics including “Faster,” effective,” and “low cost,” has become one of marketing hannels. Recommendation system have been widely adopted in the nternet area, it can help to increase customers’ purchase intention and loyalty. In this research, we adopt ecommendation technology in insurance area, and hope it can upport insurance company. Furthermore, “Flow Theory” is extensively applied in the Web environment and it can help to explain why people repetitiously use particular service. In this research, we adopt flow to inspect whether the personalized E-mail insurance recommendation work or not. According to the statement as above, in this research, we manipulate data mining technology to create a personalized E- ail insurance produces recommendation system, and we adopt the online questionnaire to find out the causal relationship among personalized E-mail insurance recommendation attributes, experiential flow, and personalized E-mail nsurance recommendation performances.
In this research, objects are people living in Taiwan, more han 20 years old and are used to use the E-mail. We adopt double sampling to collect 130 usable questionnaires and the esponse rate is 78.78 percent. The analysis methods in this research are analysis of frequency distribution, factor nalysis, Cronbach’s α and structural equation model.
The important results of this research are as follows:
1. We can predict suitable insurance products to new customers with the data mining developed personalized insurance recommendation based on insurance company’s database.
2. Customers are in the flow state when they reading the personalized E-mail insurance recommendation provided by nsurance company.
3. The flow experience has direct impact on the personalized E-mail insurance recommendation performances.

摘要..............................................................................................................I
ABSTRACT ..................................................................................................III
誌謝............................................................................V
LIST OF FIGURES.........................................................VIII
LIST OF TABLES ...............................................IX
CHAPTER 1 INTRODUCTION........................................................................................... 1
1.1 Research Background and Motivation ...........................................................................................1
1.2 Objectives......................................................................................................................................3
1.3 Research Scope and Object ............................................................................................................3
1.4 Thesis Organization.......................................................................................................................4
1.5 Research Procedure.......................................................................................................................5
CHAPTER 2 LITERATURE REVIEW............................................................................... 7
2.1 Insurance .......................................................................................................................................8
2.1.1 Insurance classification .................................................................................................................. 8
2.1.2 Insurance marketing...................................................................................................................... 9
2.1.3 Previous researches of insurance ................................................................................................. 12
2.2 Experiential Flow.........................................................................................................................14
2.2.1 Flow as an optimal experience..................................................................................................... 14
2.2.2 Flow experience on the web......................................................................................................... 17
2.2.3 Customer loyalty and optimal experience.................................................................................... 18
2.2.4 Conceptual models of flow .......................................................................................................... 19
2.3 Personalized Recommendation .....................................................................................22
2.3.1 Data mining................................................................................................................................. 23
2.3.2 Using data mining to predict customers’ preferences for different product categories ................ 24
2.4 E-mail Marketing .........................................................................................................................26
CHAPTER 3 AN INTERACTIVE PERSONALIZED INSURANCE RECOMMENDATION SYSTEM..... 29
3.1 Personalized Insurance Products Recommendation.....................................................................31
3.1.1 Procedure of personalized insurance products recommendation ................................................. 31
3.2 Personalized Health Care Information Recommendation ............................................................33
3.2.1 Procedure of personalized health care information recommendation .......................................... 33
3.3 Generation of Interactive Personalized E-mail Insurance Recommendation ...............................35
CHAPTER 4 RESEARCH DESIGN..................................................................................................................... 39
4.1 Research Framework and Hypothesis ..........................................................................................39
4.2 Operational Definitions and Measure of Variables.......................................................................40
4.2.1 E-mail insurance recommendation attributes............................................................................... 40
4.2.2 Experiential flow............................................................................................................. 44
4.2.3 Performances of E-mail insurance recommendation............................................................... 47
4.3 Samples and Procedure ....................................................................................50
4.4 Data Analysis ..................................................................................................................51
4.4.1 Reliability and validity............................................................................................................ 53
4.4.2 Structural equation model (SEM) ............................................................................................. 53
CHAPTER 5 EXPERIMENTAL RESULTS ............................................................................................ 57
5.1 Personalized Insurance Recommendation by Data Mining......................................................57
5.2 Results of Data Analysis ........................................................................................................65
5.2.1 Response rate ....................................................................................................................... 65
5.2.2 Descriptive statistics ....................................................................................................... 66
5.2.3 Exploratory factor analysis (EFA)................................................................................................ 72
5.2.4 Results of reliability and validity ................................................................................................. 79
5.3 Results of Structural Equation Modeling .....................................................................................80
5.3.1 Measures of model fit .............................................................................................................. 83
5.3.2 Research findings..................................................................................................................... 83
CHAPTER 6 CONCLUSIONS AND SUGGESTIONS..................................................... 87
6.1 Discussions and Conclusions .......................................................................................................87
6.1.1 Using E-mail insurance recommendation attributes to induce experiential flow......................... 87
6.1.2 The consequences of flow: improving E-mail insurance recommendation performance or not .. 91
6.2 Suggestions for Future Research...............................................................................................94
6.3 Research Limitation ...............................................................................................95
BIBLI.................................................................................................................... 96
APPENDIX A. QUESTIONNAIRE: INDIVIDUAL DATA.......................................................................... 108
APPENDIX B. PERSONALIZED INSURANCE RECOMMENDATION BY E-MAIL................................110
APPENDIX C. QUESTIONNAIRE: E-MAIL INSURANCE RECOMMENDATION ..................................112

[1] Agrawal, R., Imielinski, T. and Swami, A. (1993), “Mining Association Rules between Sets of Items in Large Databases,” Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp.207-214.
[2] Arbuckle, J. L. and Wothke, W. (1999), “Amos 4.0 User''s Guide,” Chicago, IL: Smallwaters.
[3] Bentler, P. M. (1990), “Comparative Fit Indexes in Structural Models,” Psychological Bulletin, Vol.107, pp.238-246.
[4] BoldFish Co. (2000), “Top Ten Strategies for Direct E-mail Success,” white paper, pp.1-7. ( http://www.boldfish.com/ )
[5] Bollen, K. A. (1989), “A New Incremental Fit Index for General Structural Equation Models,” Sociological Methods and Research, Vol.17, pp.303-316.
[6] Bollen, K. A. and Long, J. S. [Eds.] (1993), Testing structural equation models, Newbury Park, California: Sage.
[7] Bonlett, M. (2001), “Personalization of Web Services: Opportunities and Challenges,” Ariadne Issue 28. Originating URL:www.ariadne.ac.uk /issue28/personalization/intro.html
[8] Browne, M. W. and Cudeck, R. (1993), “Alternative Ways of Assessing Model Fit,” In Bollen, K. A. and Long, J. S. [Eds.] Testing Structural Equation Models. Newbury Park, California: Sage, pp.136-162.
[9] Bryant, J. and Love, C. (1996), “Entertainment as the Driver of New Information Technology,” In R. R. Dholakia, N. Mundorf, and N. Dholakia (Eds.), New infotainment technologies in the home: demand-side perspective, pp.91-114. Mahwah, NJ: Lawrence Erlbaum Associates.
[10] Burgoon, J. K., Bonito, J. A., Bengtsson, B., and Ramirez J. A. (1999/2000). “Testing the interactivity model: communication processes, partner assessments, and the quality of collaborative work,” Journal of Management Information Systems, Vol.16, No.3, pp.33-56.
[11] Burke, R. (1998), “Real Shopping in a Virtual Store,” in Sense and Respond: Capturing Value in the Network Era, [Eds.] Stephen P. B. and Nolan, R. L. Boston, MA: Harvard Business School Press, pp. 245-260.
[12] Carmines, E. G. and Zeller, R. A. (1979), “Reliability and Validity Assessment,” Sage University papers on quantitative applications in the social sciences, Vol.17, Beverly Hills.
[13] Carmines, E. G. and Mclver, J. P. (1981), “Analyzing Models with Unobserved Variables,” In Bohrnstedt, G. W. and Borgatta, E. F. [Eds.] Social Measurement: Current Issues. Beverly Hills: Sage.
[14] Chang, S. X. (2000), Research Method (AMOS and LISREL), Publish by Tsanghai Bookstore.
[15] Changchien, S. W. and Liao, H. Y. (2003), “A Market Segmentation Method which Defines Levels of Belonging to Multiple Market Segments,” Proceedings of the Ninth Information Management Research and Practice Conference, ChangHwa, Taiwan.
[16] Charla, M. (2002), “Understanding the Online Vustomer: A Typology of Online Relational Norms and Behavior,” Journal of interactive marketing, Vol.16, No.1.
[17] Chen, H., Wigand, R. T. and Nilan, M. S. (1999), “Optimal Experience of Web Activities. School of Information Studies,” School of Information Studies, Syracuse University.
[18] Chen, J. H. (2003), “A study of consumer’s preference for the distribution systems of the insurance marketing,” Thesis for Tam-Kang University, the Degree of Master of Department of insurance.
[19] Chen, Y. Z. (1996), “Insurance: Theory and Application,” Published by Sanmin Bookstore.
[20] Cheng, M. H. (2002), “Life Insurance Product the relationship between Service Quality and Customer Post-Purchase Intentions,” Thesis for Feng-Chia University, Degree of Master of Development of Insurance.
[21] Choi, D. and Kim, J. (2004), “Why People Continue to Play Online Games: In Search of Critical Design Factors to Increase Customer Loyalty to Online Contents,” CyberPsychology and Behavior, Vol.7, No.1, pp.11-24.
[22] Clarke, S. G. and Haworth, J. T. (1994), “’Flow experience in the Daily Lives of Sixth-Form Collect Students,” British Journal of Psychology, Vol.85, pp.511-523.
[23] Csikszentmihalyi, M. (1975), “Beyond Boredom and Anxiety,” San Francisco: Jossey-Bass.
[24] Csikszentmihalyi, M. (1977), “Beyond boredom and anxiety,” The Jossey-Bass Behavioral Science Series (1st Ed.). San Francisco: Jossey-Bass Publishers.
[25] Csikszentmihalyi, M. (1990), “Flow: The Psychology of Optimal Experience,” New York: Harper and Row.
[26] Cupach, W. R. and Carson, M. J. (2002), “The Influence of Compensation on Product Recommendations Made by Insurance Agents,” Journal of Business Ethics, Vol.40, No.2, ABI/INFORM Global.
[27] Davis, F. D. (1989), “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, Vol.13, No.3, pp.319-340.
[28] De, R. K., Martin W. and Bloemer J. (1998), “On the Relationship between Perceived Service Quality, Service Loyalty and Switching Costs,”International Journal of Service Industry Management, Vol.9, No.5, pp.436-453.
[29] Donald, R. J. (1995), “Coming of Age: A Snapshop of Insurance Direct Marketing and its Future,” Direct Marketing, pp.14-16.
[30] Doug, R. (2000), “Personalized Views of Personalization Association for Computing Machinery,” Communication of the ACM, Vol.43, No.8, pp.26.
[31] Dutta, K., Datta A. and VanderMeer D. (2000), “Demonstration: Enabling Scalable Online Personalization on the Web,” The VLDB Journal, pp.631-634.
[32] E-commence and Development Report. (2002), United Nations Conference on Trade and Development, Internet Version Prepared by the UNCTAD Secretariat.
[33] Ellis, G. D., Voelkl, J. E. and Morris, C. (1994), “Measurement and Analysis Issues with Explanation of Variance in Daily Experience Using the Flow Model,” Journal of Leisure Research, Vol.26, No.4, pp.337-356.
[34] Ester, M., Krirgel, H. P. and Xu, X. (1995), “A Database Interface for
Clustering in Large Spatial Database,” Proceedings of the International
Conference on Knowledge Discovery and Data Mining.
[35] Feng, W. Z. (2000), “Collaborative Recommender System for Web Site,” Thesis for National Chiao-Tung University, the Degree of Master of Development of Computer and Information Science.
[36] Fred, S. (1993), “An Examination of the Effect of Product Performance on Brand Reputation, Satisfaction and Loyalty,” European Journal of Marketing, Vol.27, No.9.
[37] Frederick, F. R. (1993), “Loyalty-Based Management,” Harvard Business School Review, pp.64-73.
[38] Ghani, J. A. (1991), “Flow in Human-Computer Interactions: Test of a Model,” In J. Garey (Eds.), Factors in Management Information Systems: An Organizational Perspective. Norwood, NJ: Ablex.
[39] Ha, L. and James, E. L. (1998), “Interactivity Reexamined: a Baseline Analysis of Early Business Web Sites,” Journal of Broadcasting and Electronic Media, Vol.42, No.4, pp.457-474.
[40] Hoffman, K. D., Howe, V. and Hardigree, D. W. (1991), “Ethical Dilemmas Faced in the Selling of Complex Services,” Journal of Personal Selling and Sales Management. Vol.11, No.4, pp.13-25.
[41] Hoffman, D. L. and Novak, T. P. (1996), “Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations,” Journal of Marketing, Vol.60, pp.50-68.
[42] Huang, C. L. (2000), Journal of Risk Management and Insurance, Vol.139, July, pp.35.
[43] Huang, M. H. (2003), “Designing Website Attributes to Induce Experiential Encounters,” Computers in Human Behavior, Vol.19, pp.425-442.
[44] Jackson, D. B. (1989), “Secretes of Insurance Direct Marketing Practices Revealed,” Nopoly Press, Wilmington , Vol.5.
[45] James, L. A. and Werner, W. (2003), “Amos 5.0 User’s Guide,” SPSS Inc. [46] Jöreskog, K. G. and Sörbom, D. (1984), “LISREL-VI User’s Guide (3rd Ed.),” Mooresville, IN: Scientific Software.
[47] Joseph, A. K., Miller, N. B., Maltz, D., Herlocker, L. J., Lee, R. G. and Riedl, J. (1997), “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of the ACM, Vol.40, No.3, pp.77-87.
[48] Juang, M. H. (2000), “Effect of e-clerks on the Intention of Purchase in Electronic,” Thesis for National Yat-Sen University, the Degree of Master of Development of Information Management.
[49] Kahneman, D. (1973), “Attention and Effort,” Englewood Cli.s, NJ: Prentice-Hall.
[50] Kalakota R. and Whinston A. B. (1996), “Frontiers of Electronic Commerce,” Addison-Wesley, Publishing Company.
[51] Kidder, L. H., Judd, C. M. and Smith, E. R. (1986), “Research Methods in Social Relations,” (5th Ed.).
[52] Kohavi, R. and Provost F. (2001), “Applications of Data Mining to Electronic Commerce,” (introductory article), Data Mining and Knowledge Discovery, Vo.1, No.5, pp.1-2.
[53] Konstan, J. A., Miller, B. M., Maltz, D., Herlocker J. L., Gordon, L. R. and GroupLens, J. R. (1997), “Applying Collaborative Filtering to Usenet News,” Communications of the ACM, Vol.40, No.3, pp.77-87.
[54] Kotler, P. and Armstrong, G. (1989), “Marketing: An Introduction,” New Jersey: Prentice Hall.
[55] Kudyba, S. and Hoptroff, R. (1991), “Data Mining and Business Intelligence : A Guide to Productivity,” Hershey, Pa. Idea Group Publishing.
[56] Lederer, A. L. and Sethi, V. (1991), “Critical Dimensions of Strategic Information Systems Planning,” Decision Sciences, Vol.22, pp.104-119.
[57] LeFevre. J. (1988), “Flow and the Quality of Experience during Work and Leisure.” In M. Csikszentmihalyi and I. Csikszentmihalyi (Eds.), Optimal Experience: Psychological Studies of Flow in Consciousness, New York: Cambridge University Press.
[58] Li, H., Kuo, C. and Russell, M. G. (1999), “The Impact of Perceived Channel Utilities, Shopping Orientations, and Demographics on the Consumer’s Online Buying Behavior,” Journal of Computer-Mediated Communication, Vol.5, No.2.
[59] Li, M. H. and Wang Q. P. (2001), “Application of E-Commerce and Life Insurance- Travel Accident Insurance.” Final report of Chaoyang University of Technology, Development of Finance.
[60] Lin, M. X., Wen, F. Y., Chang, Y. C., Zhou, Z. C. and Chung Z. Y. (2002), “A Study of the Factors of Affects College Students to Insure Online,” Final report of Chaoyang university of Technology, Development of Insurance.
[61] Loewenstein, G. (1994), “The Psychology of Curiosity: a Review and Reinterpretation.” Psychological Bulletin, Vol.116, No.1, 75- 98.
[62] Lu, T. C. (2001), “Mining Association Rules from Databases: Methods and Applications in Business and Bio-informatics,” Thesis of Chao-yang University of Technology, Degree of Master of Department of Information Management.
[63] Lutz, R. J. and Guiry, M. (1994), “Intense Consumption Experiences: Peaks, Performances, and Flows,” Winter Marketing Educators’ Conference, St. Petersburg, FL, February.
[64] MacCallum, R. C. (1990), “The Need for Alternative Measures of Fit in Covariance Structural Analysis,” Multivariate Behavioral Research, Vol.25, pp.157-162.
[65] Macneil, I. R. (1980), “The New Social Contract: an Inquiry into Modern Contractual Relations,” New Haven and London: Yale University Press.
[66] Mannell, R. C., Zuzanek, J. and Larson, R. (1988), “ Leisure States and Flow Experiences: Testing Perceived Freedom and Intrinsic Motivation Hypotheses,” Journal of Leisure RESEARCH, Vol.20, No.4, pp.289-304.
[67] Massimini, F. and Carli, W.(1988), “The Systematic Assessment of Flow in Daily Experience.” In M. Csikszentmihalyi and I. Csikszentmihalyi (Eds.), Optimal Experience: Psychological Studies of Flow in Consciousness, pp.
288-306, New York: Cambridge University Press.
[68] Methlie, L. B. and Nysveen, H. (1999), “Loyalty of On-Line Bank Customers,” Journal of Information Technology- special issue on Electronic Commerce, Vol.14, pp.375-386
[69] Miles, T., Wayne, J. and Scott, B. (2002), “Guidelines on Electronic Mail Security,” National Institute of Standards and Technology Gaithersburg.
[70] Mulail, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S. and Stilwell, C. D.(1989), “Evaluation of Goodness-of-fit Indices for Structural Equation Models,” Psychological Bulletin, Vol.105, pp.430-445.
[71] Nakamura, J. (1988), “Optimal Experience and the Uses of Talent,” In. M. Csikszentmihalyi and I. Csikszentmihalyi (Eds.), Optimal Experience: Psychological Studies of Flow in Consciousness, pp. 319-326, New York: Cambridge University Press.
[72] Novak, T. P., Hoffman, D. L. and Yung, Y. F. (1997), “Measuring the Flow Experience among Web Users,” Interval Research Corporation, Vanderbilt University.
[73] Novak, T. P., Hoffman, D. L. and Yung, Y. F. (1998), “Measuring the Flow Construct in Online Environments: A Structural Modeling Approach,” Owen Graduate School of Management, Vanderbilt University and L.L.
Thurstone Psychometric Laboratory, University of North Carolina, Chapel Hill. Working paper.
[74] Novak, T. P., Hoffman, D. L. and Yung, Y. F. (2000), “Measuring the customer experience in online environments: a structural modeling approach.” Marketing Science, Vol.19, pp.22-42.
[75] Nunnally, J. C. (1978), “Psychometric Theory,” New York: McGraw-Hill.
[76] Nysveen, H. (1999), “Ad effects on attitude towards advertised product: A
comparison of ads presented in various media (in Norwegian),” Norwegian School of Economics and Business Administration, Bergen.
[77] Piatesky-Shapire, G., and Frawley, W. (1991), “Knowledge Discovery in Database.” AAAI/MIT.
[78] Posnock, R. (1991), “The trail of curiosity: Henry James, William James, and the Challenge of Modernity,” New York: Oxford University Press.
[79] Privette, Gay and Bundrick M. C. (1987), “Measurement of Experience: Construct and Content Validity of the Experience Questionnaire,” Perceptual and Motor Skills, Vol.65, pp.315-332.
[80] Reichheld, F. F. and Sasser. W. E. (1990), “Zero Defections: Quality Comes to Service,” Harvard Business Review, Vol.68: pp.105-107, September-October.
[81] Reichheld, F. F. and Schefer, P. (2000), “E-Loyalty: Your Secret weapon on the web,” Harward Business Review, July-August, pp.105-113.
[82] Riecken, D. (2000), “Introduction: Personalized Views of Personalization,” Communications of the ACM, Vol.43, No.8, pp.26 -28.
[83] Rogers, E. M. (1995). Di.usion of innovations. New York: Free Press.
[84] Roscoe, J. R. (1975), “Fundamental Research Statistics for the Behavioral Sciences,” (2nd Ed.) New York: Holt, Rinehart and Winston.
[85] Schafer, J. B., Konstan, J. and Riedl, J. (2000), “Recommender Systems in E-Commerce,” ACM E-Commerce.
[86] Schaer, B. J., Konstan, A. J. and Riedl, J. (2001), “E-Commerce Recommendation Applications,” Data Mining and Knowledge Discovery, pp.115- 153.
[87] Schiffman, L. G. and Kanuk, L. L. (2000), “Consumer Behavior,” 7th ed., Prentice Hell International, Inc.
[88] Shamdasani, P. N. and Ong, G. Y. (1995), “An Exploration Study of In-Home Shoppers in a Concentrated Retail Market,” Journal of Retailing and Consumer Services, Vol.2, No.1, pp.15-23.
[89] Steiger, J. H. (1989), EzPHTH: Causal modeling. Evanston, IL:Systat.
[90] Tanaka, J. S. and Huba, G. J. (1985), “A Fit Index for Covariance Models under Arbitrary GLS Estimation,” British Journal of Mathematical and Statistical Psychology, Vol.38, pp.197-201.
[91] Trevino, L. K. and Webster, J. (1992), “Flow in Computer-Mediated Communication,” Communication Research, Vol.19, No.5, pp.539-573.
[92] Venkatesh, V. and Davis, F. D. (2000), “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” Management Science, Vol.46, pp.186-204.
[93] Wells, A. (1988), “Self-Esteem and Optimal Experience,” In. M. Csikszentmihalyi and I. Csikszentmihalyi (Eds.), Optimal Experience: Psychological Studies of Flow in Consciousnes, pp.319-326, New York: Cambridge University Press.
[94] Wang, H. I. (1996), “Internet Marketing of the Life Insurance Industry on the Internet,” Thesis of Feng-Cnia University, degree of master of development of Insurance.
[95] Webster, J., Trevino, L. K. and Ryan, L. (1992), “Flow in Computer- Mediated Communication: Electronic Mail and Voice Mail Evaluation and Impacts.” Communication Research, Vol.19 No.5, pp.539-573.
[96] Webster, J., Trevino, L. K. and Ryan, L. (1993), “The Dimensionality and Correlates of Flow in Human-Computer Interactions,” Computers in Human Behavior, Vol.9, pp.411-426.
[97] Weng, S. S. and Liu, M. J. (2004), “Feature-Based Recommendations for One-to-One Marketing,” Expert Systems with Applications, Vol.26, pp.493-508.
[98] Willcocks, L. P. and Plant, R. (2001), “Pathways to E-Business Leadership: Getting Frombricks and Clicks,” MIT Sloan Management Review, Vol.42, pp.50-59.
[99] Xin, X. Z. (2002), “The Study of Life Insurance Marketing Channels and Effect in Taiwan,” Thesis of Shih-Chien University, Degree of Master of Development of Business Administration.
[100] Yu, M. C. (2003), “A Thesis Submitted to Institute of Computer and Information Science College of Electrical Engineering and Computer Science.” Thesis of National Chiao-Tung University, Degree of Master of Development of Computer and Information Science.
[101] Yu, P. S. (1999), “Data Mining and Personalization Technologies,” IBM T. J. Watson Research Center.
[102] Yu, S. P. (1999), “Data Mining and Personalization Technologies,” IEEE.
[103] Zeitham, V. A., Leonard, L. B. and Parasuraman. (1996), “The Behavioral Consequences of Service Quality,” Journal of Marketing, Vol.60, No.2, pp.31-46.