-------- Original Message -------- Subject: [AISWorld] Publication of Vol.11, No.3, 2010 issue of Journal of Electronic Commerce Research Date: Mon, 30 Aug 2010 12:36:28 -0700 From: Melody Kiang mkiang@csulb.edu To: aisworld@lists.aisnet.org
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.
_______________________________________________________________________________________________ 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.
************************************************************************************************ 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.
***************************************************************************************** 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.
************************************************************************************* 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.
************************************************************************************ 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
_______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org