143.txt

This complete matlab for neural network

发信人: yaomc (白头翁&山东大汉), 信区: DataMining
标  题: [合集]寻求一篇论文，谢谢！
发信站: 南京大学小百合站 (Mon Oct 22 08:16:26 2001), 站内信件

tyqqre (tyqqre) 于Fri Oct 19 09:10:18 2001提到：

A Methodology for the Characterization of Business-to-Consumer E-commerce.
谢谢！


yaomc (白头翁&山东大汉) 于Fri Oct 19 09:13:58 2001提到：

也不知道是在哪个杂志上发表的？



roamingo (漫步鸥) 于Sat Oct 20 11:43:44 2001提到：

Alfredo Vellido. A Methodology for the Characterization of 
Business-to-Consumer E-commerce. Ph.D. thesis. School of 
Computing & Mathematical Sciences, John Moores University,
Liverpool, United Kingdom 2001.
http://www.cms.livjm.ac.uk/research/snc/av_thesis.htm

OVERVIEW

The area of electronic commerce is complex and multifaceted and its 
full conceptualization is still emerging. My thesis only concerns 
the category of consumer-oriented or business-to-consumer electronic 
commerce, deemed to have considerable market potential for the future. 
The context of electronic commerce is rich both in hype and hard facts, 
and interest in it has been fuelled by the potential size of the 
world-wide market resulting from the exponential growth of Internet 
adoption. Hype is exemplified by the success of the stock market 
capitalisation of many companies involved on it, regardless their 
performance in terms of profit generation. However there is much 
factual information to support the expectation of a large market size. 

The business-to-consumer online market has yet to reach the critical 
mass to ensure its own future success. For that to happen, technological 
breakthroughs, such as the merging of Internet with TV broadcasting 
and mobile telephony, must permeate the market. Nevertheless, new 
technologies and the growth of Internet adoption might not be 
sufficient to realize the full potential of the online consumer market.
In fact, this exponential growth has also been reported as the reason 
behind a worsening of online shopping customer service that threatens 
to put at risk customer loyalty in this marketing channel. This threat
exemplifies the necessity to go beyond a merely technology-centred 
view to carefully explore customer needs and expectations. 

The exploration of online consumers' needs and expectations justifies 
a data-based analysis of their shopping behaviour, in order to provide 
a sound empirical basis for business decision making. Most of the 
existing studies in the area are basically qualitative in nature. 
Unfortunately, the quantitative research on consumer behaviour online 
is still pretty much in its infancy and there is still no quantitative 
framework for the analysis of online purchasing behaviour. This thesis 
aims to provide one such framework along the lines described in the 
following paragraph.

We aim in this thesis to put forward a complete quantitative methodology 
for the exploratory analysis of online consumers' shopping behaviour. 
This methodology comprises several stages, namely:

  Latent variable analysis: A survey data set containing a large number 
  of online behaviour-related variables, from a sample of Internet 
  users' opinions of online shopping and online vendors, is made 
  available for this research. The latent traits behind these consumer 
  opinions are to be extracted, using factor analysis and labeled 
  according to existing literature.

  Predictive modelling: The latent factors obtained in the previous part, 
  together with demographic, socio-economic and Web usage information, 
  are to be used to construct a global predictive model of the propensity 
  to buy online. 

  Cluster-based segmentation: Those factors selected as the best 
  predictors of the propensity to buy online are to be used as bases for 
  the segmentation of the online consumer market, utilizing a neural
  network-based model for data clustering and visualization: the 
  Generative Topographic Mapping (GTM). 

  Graphical modeling: Having identified the different consumer segments, 
  it will then be possible to apply Graphical Modelling separately to 
  each segment, to produce graphs representing the inter-relationships
  between the predictive factors, or segmentation bases, and between these 
  and the propensity to buy online.

According to this schema, the main goal of the thesis is to provide 
answers to the following questions:

  A latent variable structure is to be obtained from the observable 
  variables that refer to the Web users’ opinions of the online shopping 
  channel and of the online shopping vendors. Is this structure
  explainable in terms of key factors identified in previous qualitative 
  studies? 

  Which of those latent factors contain the necessary information to best 
  predict Web users’propensity to buy on-line? 

  Are variables, such as age, household income, and Web usage patterns, 
  commonly used in market research, good predictors of that propensity? 
                  
  To what extent can the propensity to buy online be inferred, using 
  quantitative methods, from a selection of the latent factors shown 
  to be the best predictors of such a propensity and how does it
  compare with the prediction obtained from the complete set of factors? 
                  
  Can the e-commerce consumer market be segmented using latent factors 
  as segmentation variables, in such a way that the resulting segments 
  have a sensible managerial interpretation in terms of those factors 
  and in terms of secondary information? 

  Is the neural network-based unsupervised model proposed in this thesis 
  suitable for the task of segmentation of the online market? 

  Can graphical modeling provide further insight into the relational 
  structure of the factors for each of the market segments? 



tyqqre (tyqqre) 于Sat Oct 20 14:39:58 2001提到：



schummi (大头) 于Sun Oct 21 17:06:48 2001)
提到：

太好了
让我也看看