Comparison Shopping Agents and Online Price Dispersion: A Search Cost based Explanation

Search costs and consumer heterogeneity are two important explanations for the price dispersion in the brick and mortar (B&M) markets. Comparison shopping agents (CSAs) provide a single click decision support for consumers’ purchasing related decision problems and reduce their search costs by providing detail price dispersion related information. Contemporary researchers in IS observe that even with such negligible search costs, price dispersion still continues in the online markets. Consumer heterogeneity and retailer heterogeneity have been agreed upon as two primary explanations for online price dispersions. In this paper, popular CSAs are analyzed to check if they provide complete and accurate price dispersion information. It is shown that because of the selection bias and temporal delay in updating information, contemporary CSAs may not present complete and accurate price dispersion information. In order to reach to an optimal purchasing decision, consumers may have to rely on a sequential search across multiple CSAs or browse through various retailers. This research adds a search cost dimension to explain the continuance of price dispersion in the online markets

Achieving Information Integration in Supply Chain Management Through E-Hubs: Concepts and Analysis

While supply chain integration is achieved at three levels: information, resources and organization, the emphasis of the paper is placed on how information integration can be achieved through B2B e-hubs. After reviewing how e-hubs have evolved since its inception, we examine three groups of e-hubs classified by supply chain processes, namely procurement, transportation and customer relationship management, then a value-gap analysis is performed to identify the values added by the e-hubs and their potential gaps and limitations. Finally, we present a framework for integrating existing e-hubs in order to expand their functionality to provide a better solution to supply chain integration

Author Experiences with the IS Journal Review Process

Research publication in peer-reviewed journals is an important avenue for knowledge dissemination. However, information on journal review process metrics are often not available to prospective authors, which may preclude effective targeting of their research work to appropriate outlets. We study these metrics for information systems (IS) researchers through a survey of actual author experiences of the IS journal review process. Our results provide a knowledge base of the length and quality of the review process in various journals; responsiveness of the journal office and publication delay; and correlations of metrics with published studies of journal rankings. The data should enable authors to make effective submission decisions, as well as help to benchmark journal review processes among competing journals

Integrative models and empirical analysis of recommender systems in online retailing

Online retailers are increasingly utilizing recommender systems to offer product recommendations to consumers. Such recommendations are typically based on previous purchases made by a network of customers with related purchase patterns. Although there has been extensive research devoted to enhancing the quality of recommendations, little research has been done in integrating recommendations with economic factors that drive the purchase behavior. Moreover, much of the work to date has utilized data collected from user satisfaction surveys or simulated experiments to assess the impact of recommender systems. This dissertation adds to the growing literature in recommender systems by providing models to integrate other economic factors along with recommendations and by empirically investigating the performance of online recommendations. ^ The first essay of this dissertation presents integer programming based models to integrate recommendations with economic factors related to consumer purchase behavior. The underlying contention is that even if recommendations are accurate and useful, customers may not be interested in purchasing if such recommendations are not properly aligned with their economic interests. Integer programming models developed in this essay deal with integrating various economic incentives such as online retail promotions and price discounts with recommendations. These models are complementary to current recommender system algorithms and can be implemented by online retailers, or, by other independent intermediaries such as shopbots. The empirical analysis for these models suggests that our alternative set of recommendations offer significantly higher economic benefits to customers. ^ The second essay empirically analyzes the relationship between sales and recommendations. The analysis is based on a panel data of books collected from publicly available information from online retailers. A weighted measure for recommendations is developed based on the number and the impact of recommenders. Subsequently, pooled OLS and panel data based models are used to analyze the effect of recommendations on sales. Further, sales, price and recommendations are jointly determined using a simultaneous equations model to address the potential endogeneity arising from simultaneity amongst these variables. We estimate the marginal change in sales due to recommendations, and contrast it with the impacts of other types of customer feedback.

Author experiences with the IS journal review process

Copyright 1999, by the Association for Information Systems. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and full citation on the first page. Copyright for components of this work owned by others than the Association for Information Systems must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers for commercial use, or to redistribute to lists requires prior specific permission and/or fee. Request permission to publish from: AIS Administrative Office, P.O. Box 2712 Atlanta, GA, 30301-2712 Attn: Reprints, or via e-mail from [email protected] IS researchers often face a difficult decision in choosing publication outlets for their research work, as some review process factors are often not well-defined for particular outlets. For example, for time-critical research, a journal with quicker turn-around time (ceteris paribus) might be a better avenue for the work to reach the audience in the shortest time possible. In addition, finding such information is difficult. For example, process information for the same journal is not consistent across individuals, and even across manuscripts submitted by the same individual to a particular journal. This research focuses on quantifying certain metrics in the IS journal review process that are important, yet not well-known to prospective authors. We collected more than 1100 observations on these metrics from 307 authors who experienced the review process. This study provides an initial attempt to pool individual and anecdotal information of these factors into a knowledge repository for current researchers which may help them to make effective decisions on targeting journal outlets. Using concepts from process design and quality control literature, we determine if the review process is under control. Finally, we correlate our findings of these factors with journal rankings from published studies to detect if rankings are impacted by the factors identified by journal editors and researchers. Our results provide a knowledge base of â ¢ the length and quality of the review process in various journals; â ¢ responsiveness of the journal office and publication delay; and â ¢ correlations of metrics with published studies of journal rankings. The data should enable authors to make effective submission decisions, as well as help to benchmark journal review processes among competing journals

Empirical Analysis of the Business Value of Recommender Systems

Online retailers are increasingly using information technologies to provide value added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms that serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is the business value of online recommender systems to online retailers. We develop a robust empirical method that incorporates indirect impact of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive impact on sales. We also found empirical evidence for the reinforcing effect of sales on recommendations and for the positive impact of recommendations on prices. These results suggest that recommendations not only improve sales but also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher impact on sales tha