A methodology for the characterization of business-to-consumer E-commerce

This thesis concerns the field of business-to-consumer electronic commerce. Research on Internet consumer behaviour is still in its infancy, and a quantitative framework to characterize user profiles for e-commerce is not yet established. This study proposes a quantitative framework that uses latent variable analysis to identify the underlying traits of Internet users' opinions. Predictive models are then built to select the factors that are most predictive of the propensity to buy on-line and classify Internet users according to that propensity. This is followed by a segmentation of the online market based on that selection of factors and the deployment of segment-specific graphical models to map the interactions between factors and between these and the propensity to buy online. The novel aspects of this work can be summarised as follows: the definition of a fully quantitative methodology for the segmentation and analysis of large data sets; the description of the latent dimensions underlying consumers' opinions using quantitative methods; the definition of a principled method of marginalisation to the empirical prior, for Bayesian neural networks, to deal with the use of class-unbalanced data sets; a study of the Generative Topographic Mapping (GTM) as a principled method for market segmentation, including some developments of the model, namely: a) an entropy-based measure to compare the class-discriminatory capabilities of maps of equal dimensions; b) a Cumulative Responsibility measure to provide information on the mapping distortion and define data clusters; c) Selective Smoothing as an extended model for the regularization of the GTM training.

[1]  Pi-Sheng Deng,et al.  Automating Knowledge Acquisition and Refinement for Decision Support: A Connectionist Inductive Inference Model* , 1993 .

[2]  Paulo J. G. Lisboa,et al.  Segmentation of the on-line shopping market using neural networks , 1999 .

[3]  Shaw K. Chen,et al.  The comparative ability of self-organizing neural networks to define cluster structure , 1995 .

[4]  Zoubin Ghahramani,et al.  Variational Inference for Bayesian Mixtures of Factor Analysers , 1999, NIPS.

[5]  Samuel Kaski,et al.  Methods for interpreting a self-organized map in data analysis , 1998, ESANN.

[6]  Brian Everitt,et al.  Principles of Multivariate Analysis , 2001 .

[7]  A. Fuat Firat,et al.  From segmentation to fragmentation , 1997 .

[8]  D. N. Geary.  Mixture Models: Inference and Applications to Clustering , 1989 .

[9]  David G. Lowe,et al.  Optimized Feature Extraction and the Bayes Decision in Feed-Forward Classifier Networks , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  David J. C. MacKay,et al.  The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.

[11]  E. Wallin,et al.  Consumer personalization technologies for e-commerce on the Internet ; A taxonomy , 2000 .

[12]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[13]  A. N. Refenes,et al.  Comments on ‘neural networks: Forecasting breakthrough or passing fad’ by C. Chatfield , 1994 .

[14]  P. Green,et al.  Cluster-Based Market Segmentation: Some Further Comparisons of Alternative Approaches , 1998 .

[15]  B. Curry,et al.  Neural networks: a need for caution , 1997 .

[16]  T. S. Raghu,et al.  Marketing on the Internet - who can benefit from an online marketing approach? , 2000, Decis. Support Syst..

[17]  Shimon Schocken,et al.  Neural Networks for Decision Support: Problems and Opportunities , 1991, Decis. Support Syst..

[18]  Gino Verleye,et al.  ‘Wallbanking’, innovativeness and computer attitudes: 25–40-year-old ATM-users on the spot , 1996 .

[19]  J. Webster,et al.  Flow in Computer-Mediated Communication , 1992 .

[20]  C. Chatfield,et al.  Positive or negative , 1995 .

[21]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.

[22]  Ingoo Han,et al.  Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis , 1997 .

[23]  Donna L. Hoffman,et al.  A New Marketing Paradigm for Electronic Commerce , 1997, Inf. Soc..

[24]  V. Srinivasan,et al.  An Approach for Tracking Within-Segment Shifts in Market Shares , 1989 .

[25]  Josef A. Mazanec,et al.  Classifying tourists into market segments: a neural network approach. , 1992 .

[26]  Hagai Attias,et al.  Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.

[27]  Christopher K. I. Williams,et al.  Magnification factors for the GTM algorithm , 1997 .

[28]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[29]  Fionn Murtagh,et al.  Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering , 1995, Pattern Recognit. Lett..

[30]  Geoffrey E. Hinton,et al.  Adaptive Elastic Models for Hand-Printed Character Recognition , 1991, NIPS.

[31]  Rudy Setiono,et al.  Symbolic Rule Extraction From Neural Networks: An Application to Identifying Organizations Adopting IT , 1998, Inf. Manag..

[32]  Carlos Serrano-Cinca,et al.  Self organizing neural networks for financial diagnosis , 1996, Decision Support Systems.

[33]  Sarah Brien,et al.  Can we at last say goodbye to social class , 1970 .

[34]  Varghese S. Jacob,et al.  Comparative performance of the FSCL neural net and K-means algorithm for market segmentation , 1996 .

[35]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[36]  Kaisa Sere,et al.  Competitive financial benchmarking using self-organizing maps , 1997 .

[37]  K. Gabriel,et al.  SIMULTANEOUS TEST PROCEDURES-SOME THEORY OF MULTIPLE COMPARISONS' , 1969 .

[38]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[39]  M. Wedel,et al.  Consumer benefit segmentation using clusterwise linear regression , 1989 .

[40]  Arno Scharl,et al.  A Conceptual Research Framework for Analyzing the Evolution of Electronic Markets , 1998, Electron. Mark..

[41]  Christopher M. Bishop,et al.  Developments of the generative topographic mapping , 1998, Neurocomputing.

[42]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[43]  James R. Burns,et al.  Connectionist approaches to inexact reasoning and learning systems for executive and decision support : Conceptual design , 1993, Decis. Support Syst..

[44]  Greg M. Allenby,et al.  Modeling Household Purchase Behavior with Logistic Normal Regression , 1994 .

[45]  Anil K. Jain,et al.  A nonlinear projection method based on Kohonen's topology preserving maps , 1992, IEEE Trans. Neural Networks.

[46]  Yoram Wind,et al.  Issues and Advances in Segmentation Research , 1978 .

[47]  Michael J. Shaw,et al.  A classification approach using multi-layered neural networks , 1994, Decis. Support Syst..

[48]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[49]  Mary Ellen Gordon,et al.  Consumer attitudes towards Internet advertising , 1997 .

[50]  Bill C. Hardgrave,et al.  An improved method for developing neural networks: The case of evaluating commercial loan creditworthiness , 1996, Comput. Oper. Res..

[51]  Luiz Moutinho,et al.  ATM user attitudes: a neural network analysis , 1996 .

[52]  Maurice Mulvenna,et al.  Mining Marketing Intelligence from Internet Retailing Data: User Requirements & Business Process Description , 1999 .

[53]  A. Dawid.  Conditional Independence in Statistical Theory , 1979 .

[54]  Ingoo Han,et al.  Hybrid neural network models for bankruptcy predictions , 1996, Decis. Support Syst..