摘要
本文在顾客满意度基本理论、结构方程模型理论和顾客满意度指数测评进行分析的基础上,构建了顾客满意度测评的常规模型和拓展模型;给出了PLS算法求解的方法和步骤;提出了对网上调查问卷调查方式的几点改进措施;在对结构方程模型进行参数估计时,充分考虑了对缺失值的处理,提出了一种新的缺失值处理方法;设计和实现了顾客满意度指数测评系统。
主要研究内容与成果如下:
1.系统地阐述了本文的选题背景、研究目标、主要研究内容、方法和意义;明确了研究顾客满意度指数测评进行网上调查的可能性和必要性。
2.论文概述了顾客满意度基本概念;介绍了结构方程模型基本理论和当前几种顾客满意度的测评方法,给出了构建结构方程模型的步骤。
3.论文系统阐述了瑞典、美国、欧洲和中国等四种经典的顾客满意度指数模型;深入分析了Wold的PLS算法和Lohm?ller的PLS算法求解过程;通过对国内外顾客满意度指数模型的对比及我国的现状分析,提出了顾客满意度测评的常规模型和拓展模型。
4.论文构建了顾客满意度测评常规模型和拓展模型,重点分析了顾客满意度测评常规模型的模型设定、模型估计和模型评价;给出了PLS算法估计常规模型和拓展模型各个参数的方法和步骤;对网上调查的方法进行了概括总结,阐述了网上调查的优缺点,提出了对网上调查的几点改进措施;概括了缺失值处理的方法,提出“权重新定”的缺失值处理方法;详细分析了PLS算法的实现过程。
5.论文详细阐述了顾客满意度测评系统的设计和实现过程;分别以食品公司和汽车公司为研究对象,对构建的顾客满意度测评常规模型和拓展模型进行实证分析和评价,验证了模型的信度、效度和适合度等,测评结果是满意和有效的;该系统与SmartPLS软件在收敛性、权重系数等方面进行了比较,在迭代次数较SmartPLS少,在潜变量估计值相关系数、权重系数等方面与SmartPLS具有一致性,为中国顾客满意度测评理论研究及其应用提供了新的方法和思路。
In this dissertation, based on the theory of Customer Satisfaction, Structural Equation Model, the method of Customer Satisfaction measurement, Customer Satisfaction regular model and expanded model are constructed. It shows the method to solve two models by PLS Algorithm and put forward several improving measures to Internet Survey. The processing of missing data has been taken into account adequately about parameters estimation of Structural Equation Model. A new method of missing data is put forward. The system of Customer Satisfaction measurement is completed.
Main works of this dissertation is as follows:
1. The research background, methods, goal, and main content are described systematically. The possibility and necessity of Internet Survey are confirmed about the research of Customer Satisfaction Index Measuring.
2. The conception of Customer Satisfaction is summarized. The dissertation introduces the based theory of Structural Equation Model, presents the step of constructing Structural Equation Model, and describes the method of several Customer Satisfaction measurements.
3. Four classical enterprise customer satisfaction index models and its structure characteristic are introduced. Wold’s and Lohm?ller’s PLS Algorithm are analyzed deeply. Customer Satisfaction measurement regular model and expanded model are put forward by comparing with Customer Satisfaction measurement model at home and aboard.
4. The dissertation constructs Customer Satisfaction measurement regular model and expanded model. The setup, estimation and assessment of Customer Satisfaction regular model are analyzed mainly. It shows the method to evaluate the parameters of two models by PLS Algorithm. The advantage and disadvantage of Customer Satisfaction Internet Survey are analyzed deeply. The way of Internet Survey is improved. The missing data processing method of Weight Setup Newly (WSN) is presented. PLS Algorithm is finished based on Matlab.
5. The design and implement of Customer Satisfaction measurement system are based on the theory and algorithm mentioned above. Customer Satisfaction measurement regular model and expanded model are analyzed and applied to evaluate food company and auto company respectively. The reliability, validity and goodness-of-fit index (GFI) of models are validated. The case results prove the feasibility and validity of the system. Compared the system with SmartPLS about astringency, weight coefficient and so forth, its iterative number is less and others are consistent. A new method and train is presented about theory research and application of Customer Satisfaction measurement in China.
引文
[1] Fornell C.,刘金兰.顾客满意度与ACSI.天津:天津大学出版社,2006.
[2]陈立萍.统计建模与R软件[M].清华大学出版社,2007年.
[3]唐晓芬.顾客满意度评测[M].上海:上海科学技术出版社,2004.
[4]姜俊偲.中国民航企业顾客满意度与市场细分研究[D].天津:天津大学,2007.
[5]林盛.基于PLS结构方程模型的服务行业顾客满意度测评方法及应用研究[D].天津:天津大学,2002.
[6]安珣.面向顾客满意度改进决策的结构方程和影响图结合研究[D].天津:天津大学,2006.
[7]李勇.顾客满意度指数模型及其测评方法研究[D].北京:中国矿业大学,2008.
[8]刘新燕.顾客满意度指数模型研究[M].北京:中国财政经济出版社,2004.
[9]梁燕.中国顾客满意度指数构建若干问题研究[D].北京:中国人民大学,2004.
[10]梁燕.顾客满意度模型的样本量研究[J].统计研究,2007, 7: 68-74.
[11]林盛,刘金兰,韩文秀.基于PLS结构方程的顾客满意度评价方法[J].系统工程学报,2005, 20(6): 653-656.
[12]国家质检总局质量管理司.清华大学中国企业研究中心.中国顾客满意指数指南[M].北京:中国标准出版社,2003.
[13]严建修.顾客的满意度测量[M].北京:中国纺织出版社,2003.
[14]熊凯,王娟.服务企业顾客期望管理[J].当代财经,2005年01期.
[15]麻志宏.顾客满意度测评方法研究[D].大连:大连理工大学,2004.
[16]刘金兰.结构方程模型的偏最小二乘算法及其几何意义[J].哈尔滨商业大学学报(自然科学版),2005年12月第21卷第6期.
[17]候杰泰,温忠麟,成子娟.结构方程模型及其应用.北京:教育科学出版社,2004, 7: 12-14.
[18]葛哲学,孙志强.神经网络理论与MATLAB R2007实现[M].电子工业出版社,2007年.
[19]丛爽.面向MATLAB工具箱的神经网络理论与应用(第3版)[M].中国科学技术大学出版社,2009年.
[20]高隽编著.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2003.
[21]刘永坤.基于遗传神经网络的建筑工程快速估价研究[D].华东交通大学2006年.
[22]姜婷婷,丁宁.方兴未艾的网上调查[J].情报科学,2003年04期.
[23]徐浪,向蓉美.目前中国网络调查的局限性和适用范围[J].统计与信息论坛,2006(1): 84-88.
[24]王东.网上调查利弊的方法论权衡和评估[J].统计与信息论坛,2007年04期.
[25]熊丽.顾客满意指数模型计算及系统研究[D].武汉理工大学,2006年4月.
[26]刘燕.电子政务公众满意度测评理论、方法及应用研究[D].国防科学技术大学,2006年5月.
[27]王历容.缺失数据下两总体分位数差异的经验似然推断[D].广西师范大学,2008年3月.
[28]茅群霞.缺失值处理统计方法的模拟比较研究及应用[D].四川大学, 2005, (02).
[29]袁中萸.多元线性回归模型中缺失数据填补方法的效果比较[D].中南大学,2008年5月.
[30]金勇进.缺失数据的插补调整[J].数量统计与管理,2001(6): 47-53.
[31]宁禄乔,刘金兰.Stone-Geisser检验在顾客满意度中的应用[J].天津大学学报(社会科学版),2008, (03): 238-242.
[32]廖颖林.顾客满意度指数测评方法及其应用研究,上海财经大学出版社,2008年.
[33]刘宇,葛新权.顾客满意度测评信度与效度分析.世界标准化与质量管理,2002, (3): 22-23.
[34]王惠文.PLS路径模型在建立综合评价指数中的应用[J].系统工程理论与实践,2004(10): 80-85.
[35]康键.顾客抱怨行为与顾客满意度_顾客忠诚的关联性研究[D].天津:天津大学,2006.
[36] Wixom, B.H. & Watson, H.J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25(1), 17-41.
[37] Lohm?ller, J.-B. (1987). PLS-PC: Latent Variables Path Analysis with Partial Least Squares - Version 1.8 for PCs under MS-DOS.
[38] Li, Y. (2003). PLS-GUI User Manual– A Graphic User Interface for LVPL (PLS-PC 1.8)– Version 1.0. University of South Carolina, Columbia, SC.
[39] Li, Y. (2005). PLS-GUI– Graphic User Interface for Partial Least Squares (PLS-PC 1.8)– Version 2.0.1 beta. University of South Carolina, Columbia, SC.
[40] Fu, J.-R. (2006a). VisualPLS– Partial Least Square (PLS) Regression– An Enhanced GUI for Lvpls (PLS 1.8 PC) Version 1.04. National KaohsiungUniversity of Applied Sciences, Taiwan, ROC.
[41] Fu, J.-R. (2006b). VisualPLS– Partial Least Square (PLS) Regression– An Enhanced GUI for Lvpls (PLS 1.8 PC) Version 1.04. http://www2.kuas.edu.tw/prof/fred/vpls/index.html.
[42] Chin, W.W. PLS-Graph User’s Guide Version 3.0. C. T. Bauer College of Business, University of Houston, Houston, Texas. USA.2001.
[43] Ringle, C. M., Wende, S., and Will, A. (2005). SmartPLS– Version 2.0. Universit?t Hamburg, Hamburg.
[44] Cardonzo, Richard N.An Experimental Study of Consumer Effort, Expectation and Satisfaction. Journal of Marketing Research, 1965, (8):244-249.
[45] Howard, John A. & Jagdish N.Sheth. The Theory of Buyer Behavior. New York Wiley.1969.pp.27-28.
[46] Anderson, Consumer Dissatisfaction: The Effect of Disconfirmed Expectancy on Perceived Product Performance [J], Journal of Marketing Research, 1973, 10(1).
[47] L.Festinger, A Theory of Cognitive Dissonance [M], Stanford University Press, 1957.
[48] John E.Swan, Linda Jones Combs, Product Performance and Consumer Satisfaction: A New Concept [J], Journal of Marketing, 1976, 40-3.
[49] Hempel, Donald J.consumer Satisfaction with the Homo Buring Process: Conceptualization and Measurement. The Conceptualization and Measurement of Consumer Satisfaction and Dissatisfaction. Cambridge. MA: Marketing Science Institute, 1977, pp.7-9.
[50] Richard L. Oliver. Measurement and Evaluation of Satisfaction Processes in Retailing Setting. Journal of Retailing, 1981, Fall, pp.25-48.
[51] Churchill, Gilber A., Jr&Carol Superenant. An Investigation into the Determinants of Consumer Satisfaction.Journal of Marketing Research, 1982, Nov, pp.491-P504.
[52] Woodruff, R.B., Ernest, R.C. and Jenkins, R.L., Modeling Consumer Satisfaction Processes Using Experience-Based Norms, Journal of Marketing Research, 1983, 20(3): 296-304.
[53] Tes, David K. &Wilton, Peter C.Models of Consumer SatisfactionFormation: An Extension, Journal of Marketing, 1988, pp.204-212.
[54] Cadotte, E.R., Woodruff, R.B., and Jenkins, R.L., Exceptions and Norms in Medels of Consumer Satisfaction, Journal of Marketing Research, 1987, 24: 305-314.
[55] Woodside, A.G., Frey, L.L.and Daly, R.T., Linking Service Quality, Customer Satisfaction and Behavioral Intention, Journal of Health Care Marketing, 1989,9:5-17.
[56] Westbrook, R.A. and R.L.Oliver. The Dimensionality of Consumption Emotion Patterns and Consumer Satisfaction, Journal of Consumer Research, 1991, Vol.18, pp.84-91.
[57] Fornell, C., A National Customer Satisfaction Barometer: The Swedish Experience, Journal of Marketing, 1992, 56(1): 6-22.
[58] Gary L Lilien, Philip Kotler, K.Sridhar Moorthy. Marketing Models [M], Englewood Cliffs, NJ: Prentice Hall, 1992.
[59] Engel, James F., Toger D. Blackwell&Paul W. Miniard. Consumer Behavior, Harcourt Broce Joranovich. College Publishers. The Dryden Press, 1993.
[60] Anderson E.W., Cales Fornell, Donald R.Lehman, Customer Satisfaction, Market Share, and Profitability: Findings from Sweden, Journal of Marketing, 1994, 58: 53-66.
[61] Parasuraman, A., V.A.Zeithaml, and L.L.Berry. Reassessment of Expectations as A Comparison Standard in Measuring Service Quality: Implications for Further Research, Journal of Marketing, 1994, 58, pp.111-123.
[62] Ostrom&Iacobucci. Consumer Trade-Offs and the Evaluation of Services, Journal of Marketing. 1995, January, pp.17-28.
[63] Kotler, J.and Scheff, J., Crisis in the arts: The marketing response, California Management Review, 1996, 39(1): 28-53.
[64] Kotler, P. Marketing Management: Analysis, Planning, Implementation, and Control, Prentice-Hall, 1999.
[65] Wong, A., Integrating Supplier Satisfaction with Customer Satisfaction, Total Quality Management, 2000, 11(4-6): 826-829.
[66] Tor Wallin Andreassen, Bodil Lindestad. Customer loyalty and complex services, the impact of corporate image on quality, customer satisfaction and loyalty for customers with varying degrees of service expertise [J]. International journal of Service Industry Management, 1998, 9(1): 7-23.
[67] Richard L.Oliver. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decision [J]. Journal of Marketing Research, 1980, 18: 460-9.
[68] Richard L.Oliver. Satisfaction- A Behavioral Perspective on the Consumer [M]. Irwin/ Mc Graw-Hill, 1996.
[69] Spreng, R.A., A Comprehensive Model of the Consumer Satisfaction Formation Process, Dissertation Abstracts International, 1993, Vol.53, No.7, pp.2461-2462.
[70] Fomell, Claes. A National Customer Satisfaction Barometer: The Swedish Experience [J]. Journal of Marketing, 1992, 56(1): 6-21.
[71] Claes Fornell, Michael D.Johnson, Eugene W. Anderson, Jaesung Cha, BarbaraEveritt Bryant, The American Customer Satisfaction Index: Nature, Purpose, and Findings [J], Journal of Marketing, 1996, 60: 7-18.
[72] Eugene W. Anderson, Clares Fornell, Foundations of the American Customer Satisfaction Index [J], Total Quality Management, 2000.
[73] Sean Conlan and Andrew Hallissey. ECSI European Consumer Satisfaction Index [R]. Excellence Ireland-Irish Quality Control Association, 2000.
[74] Fornell, Claes and Birger Wernerfelt, Defensive Marketing Strategy by Customer Complaint Management: A Theoretical Analysis, Journal of Marketing Research, 1987, 24(11):337-346.
[75] Singh, Jagdip., Consumer Complaint Intentions and Behavior: Definitional and Taxonomical issues, Journal of Marketing, 1988, 52(January): 93-107.
[76] Anderson, Eugene W.&Sullivan, M.W., Customer Satisfaction and Retention Across Firms,presentation at the TIME College of Marketing Special Interest Conference on Service Marketing, 1990.
[77] Roland T.Rust, Anthony I. Zahrik. Customer Satisfaction, Customer Retention, and Market Share [J]. Journal of Retailing, 1993, 69(2): 193-214.
[78] Anders Gustafsson, Michael D. Johnson. Measuring and Managing The Satisfaction-Loyalty-performance Links at Volvo [J]. Journal of Targeting, Measurement and Analysis for Marketing, 2002, 10 (3): 249-258.
[79] WynneW.Chin. Partial Least Squares is to LISREL as Principal Components Analysis is to Common Factor Analysis [J]. Technology Studies, 1995(2): 315-319.
[80] Fornell C. Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-vioce Theory [J]. Journal of Marketing Research, 1982, 19: 440-452.
[81] Hulland, J. S. and D. N. Kleinmuntz (1994). Factors influencing the use of internal summary evaluations versus external information in choice, Journal of Behavioral Decision Making, 7(2), pp. 79-102.
[82] Smith, J. B. and D. W. Barclay (January 1997). The effects of organizational differences and trust on the effectiveness of selling partner relationships, Journal of Marketing, 61, pp. 3-21.
[83] Johansson, J.K. and G. S. Yip (1994). Exploiting globalization potential: U.S. and Japanese strategies, Strategic Management Journal, 15(8), pp. 579-601.
[84] Birkinshaw, J., A. Morrison and J. Hulland (1995). Structural and competitive determinants of a global integration strategy, Strategic Management Journal, 16(8), pp. 637-655.
[85] Guinot C., Latreille J., Tenenhaus M. PLS Path modeling and multiple table analysis. Application to the cosmetic habits of women in Ile-de-France, 2001.
[86] Fornell C. A Second Generation of Multivariate Analysis [M]. NewYork: Praeger, 1982.
[87] Fornell C., Lorange P., Roos J. The cooperative Venture Formation Process: A Latent Variable Structural Modeling Approach [J]. Management Science, 1990, 36(10): 1246-1255.
[88] Wold H. Soft modeling:The basic design and some extensions [A]. K G J reskog H Wold, Systems under Indirect Observation[C], North-Holland: Amsterdam, 1982. 1-54.
[89] Wold H. Partial least squares [A]. Kotz S Johnson N L. Encyclopedia of Statistical Sciences[M], New York: John Wiley & Sons, 1985, 6: 581-591
[90] U. Grandcolas, R. Rettie, and K. Marusenko, Web survey bias: Sample or mode effect, Journal of Marketing Management, 19(5/6), 2003, pp. 541-561.
[91] K. Kuhnert, and D.P. McCauley, Applying alternative survey methods. In A. I. Kraut (Ed.), Organizational surveys (pp. 1-17). San Francisco: Jossey-Bass, 1996.
[92] Robert R. Byrd, Exploring Internet Survey Techniques: A Study of Personal Values Leadership Style Congruence [D], University of Louisville, 2002.
[93] David S. Yeager and Jon A. Krosnick. Comparing the Accuracy of RDD Telephone Surveys and Internet Surveys Conducted with Probability and Non-Probability Samples. Stanford University. 2009.
[94] Berrens, Robert P., Alok K. Bohara, Hank Jenkins-Smith, Carol Silva, and David L. Weimer. The Advent of Internet Surveys for Political Research: A Comparison of Telephone andInternet Samples. Political Analysis 2003. 1: 1-22.
[95] Fricker, Scott, Galesic, Mirta, Tourangeau, and Yan, Ting. An Experimental Comparison of Web and Telephone Surveys, Public Opinion Quarterly, Volume 69 (2005), 370-392.
[96] Cheryl Campanella Bracken, Leo W. Jeffres, Kimberly A. Neuendorf, David Atkin. Parameter estimation validity and relationship robustness: A comparison of telephone and internet survey techniques [J], Telematics and Informatics, v.26 n.2, p.144-155, May, 2009.
[97] LinChiat Chang. A Comparison Of Samples And Response Quality Obtained From RDD Telephone Survey Methodology and Internet Survey Methodology [D]. Doctoral Dissertation, Ohio State University, Columbus, OH. 2001.
[98] LinChiat Chang, Jon A. Krosnick. 2003. National Surveys Via RDD Telephone Interviewing vs. the Internet: Comparing Sample Representativeness and Response Quality [D]. Manuscript under review.
[99] N. Cobanoglu and N. Cobanoglu, The effect of incentives in web surveys: Application and ethical considerations, International Journal of Market Research,45(4), 2003, pp. 475-488.
[100] Liaw, S.S. (2002), An Internet survey for perceptions of computer and the World Wide Web: relationship, prediction and difference, Computers in Human Behavior, Vol. 18 pp.17-35.
[101] Little R J A, Rubin D B. Statistical Analysis with Missing Data [M]. New York: John Wiley&Sons, 1987.
[102] Little R J A, Rubin D B. Statistical Analysis with Missing Data 2nd [M]. New York: John Wiley&Sons, 2002.
[103] Wang Q H, Rao J N K. Empirical likelihood for linear regression models under imputation for missing response [J]. The Canadian Journal Statistics, 2001, 29: 597-608.
[104] Gadbury GL, Coffey CS, Allison DB. Modern statistical methods for handling missing repeated measurements in obesity trial data: Beyond LOCF Obesity Rev., 2003. 4: 175-184.
[105] PstriCia A. PtriCian Foucs on Research Methods Multiple Imputation for Misssing Data. Research in Nursing & Health, 2002. 25: 76-84.
[106] Kalton, Graham. Compensating of Missing Survey Data. Research Report Series. Ann Arbor, MI: Institute for Social Research, University of Michigan. 1983.
[107] RubinDB. Inference and missing data. Biometrika, 1976, 63(3): 581-592.
[108] RubinDB.Multiple imputation: aprimer. Statistical Methods in Medical Research, 1999, 8(1): 3-15.
[109] RubinDB. Multiple imputation after 18+ years. Journal of the American Statistical Association 91, 199: 473-489.
[110] RubinDB, Sehafer JL. Multiple Imputation for Missing data Problems. Lecture, http://www.stat.psu.edu/~jls/misoftwa.html.
[111] Allison, Paul D. Multiple imputation for missing data: A cautionary tale. Sociological Methods and Research, 2000, 28: 301-309.
[112] Allison, Paul D. (2003). Missing data techniques for structural equation models. Journal of Abnormal Psychology. 112: 545-557.
[113] Tenenhaus, M., Vinzi, V.E., Chatelin, Y.M. & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48(1), 159-205.
[114] Bookstein F. L. (1982). The geometric meaning of soft modeling, with some generalizations [R]. In K. G. J?reskog & H. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction, Vol. 1. Contribution to Economic Analysis, No. 139 (pp. 55-74).
[115] Amato, S., Esposito Vinzi, V., Tenenhaus, M., 2004. A global goodness-of-fit index for PLS structural equation modeling. Oral Communication to PLS Club,HEC School of Management, France, March 24.
[116] Geisser S. A predictive approach to the random effect model [J]. Biometrika, 1974, 61: 101-107.
[117] Stone M. Cross-validatory choice and assessment of statistical predictions [J]. Journal of the Royal Statistical Society, 1975, 36: 111-133.
[118] Meyer J S, Ingersoll C G, Mcdonald L L, et al. Estimating uncertainty in population growth rates: Jackknife Vs. Bootstrap techniques [J]. Ecology, 1986, 67: 1156-1166.
[119] Maia A H N, Lulz A J, Campanhola C. Statistical inference on associated fertility life table parameters using jackknife technique: computational aspect. J.Econ. Entomol, 2000, 93(2): 511-518.
[120] Bradley Efron. Bootstrap Methods: Another Look at the Jackknife [J]. The Annals of Statistics, 1979, 7 (1): 1-26.
[121] Efron B., Tibshirani R J. An introduction to the Bootstrap [M]. 1993.
[122] S.Shamsunder, Signal Processing Applications Of The Bootstrap Signal Processing Magazine, IEEE Volume 15, Issue 1, Jan 1998 Page(s): 38-38.
[123] A.M. Zoubir and B. Boashash, The bootstrap and its application in signal processing, in IEEE Signal Processing Magazine, January 1998.
[124] Bradley E. The bootstrap and modern statistics [M]. Journal of the American Statistical Association. 2000.
[125] Matzler, K. & Renzl, B. The Relationship between Interpersonal Trust, Employee Satisfaction, and Employee Loyalty. Total Quality Management &Business Excellence, 2006, 17(10): 1261-1271.
[126] Cronbach L. J., Coefficient Alpha and the Internal Structure of Tests. Psychometrika, 1951, 16(4): 297-334.
[127] Nunnally, J. C. (1978), Psychometric theory, 2nd Edition, New York: McGraw-Hill.
[128] Hulland, J. (1999). Use of Partial Least Squares (PLS) in Strategic Management Research: A review of four recent studies. Strategic Management Journal, 20, 195-204.
[129] Fornell, C., and Larcker, D.F. Evaluating Structural Equation Models with Unobservable and Measurement Error: A Comment. Journal of Marketing Research, 18(3), 39-50.
[130] Bagozzi, R. P. & Yi, Y., (1988). On the evaluation of structure equations models, Academic of Marketing Science, Vol.16 (1), pp.76-94.
[131] Raines-Eudy, R. (2000). Using structural equation modeling to test for differential reliability and validity: An empirical demonstration. StructuralEquation Modeling, 7(1), 124-141.
[132] Chin, W. W., 1998. The partial least squares approach for structural equation modeling. In: Marcoulides, G.A. (Ed.), Modern Methods for Business Research. Lawrence Erlbaum Associates, London, pp. 295-336.
[133] Jeffrey E. Danes and O. Karl Mann. Unidimensional measurement and structural equation models with latent variables Journal of Business Research, 1984, vol. 12, issue 3, pages 337-352.
[134] Guinot Christiane, Latreille Julie, Tenenhaus Michel. PLS path modeling and multiple table analysis. Application to the cosmetichabits of women in Ile-de-France [J]. Chemometrics and Intelligent laboratory systems. 2001, 58: 247-259.
[135] Anderson, J. C. & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommends two-step approach, Psychological Bulletin, 103(3), pp.411-423.
[136] Bollen K A. Structural equations with latent variables. New York: Wiley, 1989.
[137] Namboordiri N. K., L. E. Carter, and H. M. Blalock, Jr. Applied Multivariate. Analysis and Experimental Designs. New York: McGraw-Hill. 1975.
[138] Lohm?ller, J.-B. Latent Variable Path Modeling with Partial Least Squares. Heidelberg: Physica-Verlag, 1989.