[AISWorld] Publication of Vol.11, No.3, 2010 issue of Journal of Electronic Commerce Research

[Melody Kiang]

Dear Colleagues,
    On behalf of the Journal of Electronic Commerce Research (JECR), I 
am pleased to announce that Vol. 11, Number 3, 2010 issue of JECR is 
now available at the journal web site: "http://www.jecr.org". This is 
a special issue on "Comparison-Shopping and Related Recommender 
Intelligent Agents," guest co-edited by Dr. Yun Wan, University of 
Houston - Victoria, United States and Dr. Maria Fasli, University of 
Essex, United Kingdom.

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Introduction to the Special Issue - Comparison-shopping and 
Recommendation Agents: a Research Agenda
Yun Wan              Department of Computer Science, University of 
Houston-Victoria,
United States
Maria Fasli           School of Computer Science and Electronic 
Engineering,
University of Essex, UK 
                                        175-177

This article serves as the introduction to the special issue on 
comparison-shopping and recommendation agents and provides a brief 
overview of the four papers included in the issue indicating their 
contributions. It also offers the guest editors’ perspectives on the 
future research in this field.

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A Survey of the Comparison Shopping Agent-Based Decision Support 
Systems
Bhavik Pathak         Department of Decision Science, School of 
Business and Economics
Indiana University South Bend, India 
                               178-192

ABSTRACT

The web-based comparison shopping agents (CSAs) or shopbots have 
emerged as important business intermediaries that provide decision 
support to both the shoppers and the merchants. The basic idea is to 
provide an easy access to both the price and non-price based 
competitive features to shoppers. The CSAs do not have an equivalent 
counterpart in the offline world and they have generated a significant 
amount of interest among researchers in economics, marketing, and 
information systems fields. There have been numerous studies on the 
CSAs in the contexts of price dispersion, consumer behavior, search 
costs, and recommender systems. The focus of this paper is to study 
the contemporary literature about the CSAs to analyze them in the 
context of decision support systems (DSS). In order to provide 
comprehensive decision support, a typical DSS should have four 
components: data, models, interfaces, and user specific customization. 
In this paper, this four component framework is used to synthesize the 
current research work in the context of DSS and to explore 
contemporary CSAs. The paper provides suggestions for improving the 
decision support aspect of the CSAs and proposes a research agenda for 
the CSA-based decision support systems.


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Effects of Comparison Shopping Websites on Market Performance: Does 
Market Structure Matter?
Chuan-Hoo Tan        Department of Information Systems, City 
University of Hong Kong,
  Hong Kong
Khim-Yong Goh        Department of Information Systems, National 
University of Singapore,
Singapore
Hock-Hai Teo         Department of Information Systems, National 
University of Singapore,
Singapore                                                     193-219
                                                     193-219

ABSTRACT

The presence of Comparison Shopping (CS) websites not only allows 
consumers to gain quick access to multiple merchants’ product offers, 
but also permits consumers to perform extensive comparison of products 
and prices prior to purchase. Given the significant reduction in 
search cost, it has been touted that CS websites can put merchants 
under increased price competition, resulting in commoditized markets, 
limited value of branding, and ultimately, convergence of prices to 
the competitive equilibrium. However, some studies suggest that lower 
search cost could make any price movement apparent to all 
participating merchants and hence promote price collusion. This 
research seeks to explicate the conditions under which CS websites are 
more likely or less likely to intensify market competition. Following 
the principles of experimental economics, we modeled and examined the 
impact of CS websites in many simulated markets featuring merchant 
characteristics (such as absence and presence of market power) and 
product type (such as commodity products and differentiated products). 
Through two series of experiments, we find that the lowering of search 
cost by CS websites could have opposite effects on market performance, 
depending on the underlying market structure.


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Helpful or Unhelpful: A Linear Approach for Ranking Product Reviews
Richong Zhang         School of Information Technology and 
Engineering, University of Ottawa,
Canada
Thomas Tran          School of Information Technology and Engineering, 
University of Ottawa,
Canada  	                                                220-230

ABSTRACT

Most E-commerce web sites and online communities provide interfaces 
and platforms for consumers to express their opinions about a specific 
product by writing personal reviews. The fast development of 
E-commerce has caused such a huge amount of online product reviews to 
become available to consumers that it is impossible for potential 
consumers to read through all the reviews and to make a quick 
purchasing decision. Review readers are asked to vote if a review is 
“Helpful” or “Unhelpful” and the most positively voted reviews are 
placed on the top of product review list. However, the accumulation of 
votes takes time for a review to be fully voted and newly published 
reviews are always listed at the bottom of the review list. This paper 
proposes a linear model to predict the helpfulness of online product 
reviews. Reviews can be quickly ranked and classified by our model and 
reviews that may help consumers better than others will be retrieved. 
We compare our model with several machine learning classification 
algorithms and our experimental results show that our approach 
effectively classifies online reviews. Also, we provide an evaluation 
measurement to judge the performance of the helpfulness modeling 
algorithm and the results show that the helpfulness scores predicted 
by our approach consistently follow the changing trend of the true 
helpfulness values.

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Legal Challenges and Strategies for Comparison Shopping and Data Reuse
Hongwei Zhu          College of Business and Public Administration, 
Old Dominion University,
United States
Stuart E. Madnick      Sloan School of Management, Massachusetts 
Institute of Technology,
United States                                                  231-239

ABSTRACT

New technologies have been continuously emerging to enable effective 
reuse of an ever-growing amount of data on the Web. Innovative firms 
can leverage the available technologies and data to provide useful 
services. Comparison-shopping services are an example of reusing 
existing data to make bargain-finding easier. Certain reuses have 
caused conflicts with the firms whose data has been reused. Countries 
in the European Union have implemented the Database Directive to 
provide legal protection for database creators, but the impact and the 
interpretation of the new law are unclear and still evolving. 
Lawmakers in the U.S. have not decided on a policy concerning database 
protection and data reuse. Both data creating and data reusing firms 
need to develop strategies to operate effectively in this uncertain 
environment. Comparison-shopping and other data reuse services face 
similar legal and strategic challenges. Thus we address these 
challenges in the broader data reuse context. We use economic 
reasoning to formulate strategies in anticipation of the likely policy 
choices and interpretations of existing legislation. Both data 
creating firms and data reusing firms should focus on innovative ways 
of using or reusing data to create differentiated products and 
services. For firms that gather data from multiple sources, they can 
also use the insights gained from integrated data to provide other 
value-added services.



Dr. Melody Kiang
Professor,
Information Systems Department
College of Business Administration
California State University at Long Beach
Long Beach, CA 90840
Tel: 562-985-8944
Fax: 562-985-5478