Paulo B. Góes

GIST: a model for design and management of content and interactivity of customer-centric web sites

Customer-centric Web-based systems, such as e-commerce Web sites, or sites that support customer relationship management (CRM) activities, are themselves information systems, but their design and maintenance need to follow vastly different approaches from the traditional systems lifecycle approach. Based on marketing frameworks that are applicable to the online world, and following design science principles, we develop a model to guide the design and the continuous management of such sites. The model makes extensive use of current technologies for tracking the customers and their behaviors, and combines elements of data mining and statistical analyses. A case study based on a financial services Web site is used to provide a preliminary validation and design evaluation of our approach. The case study showed considerable measured improvement in the effectiveness of the companys Web site. In addition, it also highlighted an important benefit of the our approach: the identification of previously unknown or unexpected segments of visitors. This finding can lead to promising new business opportunities.

“Popularity Effect” in User-Generated Content: Evidence from Online Product Reviews

Online product reviews are increasingly important for consumer decisions, yet we still know little about how reviews are generated in the first place. In an effort to gather more reviews, many websites encourage user interactions such as allowing one user to subscribe to another. Do these interactions actually facilitate the generation of product reviews? More importantly, what kind of reviews do such interactions induce? We study these questions using data from one of the largest product review websites where users can subscribe to one another. By applying both panel data and a flexible matching method, we find that as users become more popular, they produce more reviews and more objective reviews; however, their numeric ratings also systematically change and become more negative and more varied. Such trade-off has not been previously documented and has important implications for both product review and other user-generated content websites.

A theoretical and empirical investigation of multi-item on-line auctions

In this paper we explore and analyze the structure of Internet auctions from an analytical and an empirical perspective. Such web‐based auctions are rapidly emerging as a mercantile process of choice in the electronic marketplace. We observe current Internet auctions for one‐time products, such as rapidly aging hardware, and analyze them within the framework of the existing auction theory. While traditional auction theory focuses on single‐item auctions, we observe that a majority of on‐line auctions are multi‐item auctions. A significant contribution of this work is the theoretical derivation of the structure of the winning bids in multi‐item progressive on‐line auctions. Additionally, for comparative purposes, we explore the structural characteristics of alternative multi‐item auction mechanisms proposed in the auction theory. We derive hypotheses based on our analytical results and compare two different types of auction mechanisms. We test the traditional auction theory assumption regarding the homogeneity of bidders and present the first ever empirically derived classification and performance‐comparison of on‐line bidders. We test our hypotheses using real‐world empirical data obtained by tracking a premier web‐based auction site. Statistical analysis of the data indicates that firms may gain by choosing alternative auction mechanisms. We also provide directions for further exploration of this emerging but important dimension of electronic commerce.

Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline

“Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.

Privacy Protection of Binary Confidential Data Against Deterministic, Stochastic, and Insider Threat

A practical model and an associated method are developed for providing consistent, deterministically correct responses to ad-hoc queries to a database containing a field of binary confidential data. COUNT queries, i.e., the number of selected subjects whose confidential datum is positive, are to be answered. Exact answers may allow users to determine an individuals confidential information. Instead, the proposed technique gives responses in the form of a number plus a guarantee so that the user can determine an interval that is sure to contain the exact answer. At the same time, the method is also able to provide both deterministic and stochastic protection of the confidential data to the subjects of the database. Insider threat is defined precisely and a simple option for defense against it is given. Computational results on a simulated database are very encouraging in that most queries are answered with tight intervals, and that the quality of the responses improves with the number of subjects identified by the query. Thus the results are very appropriate for the very large databases prevalent in business and governmental organizations. The technique is very efficient in terms of both time and storage requirements, and is readily scalable and implementable.

Optimal Design of the Online Auction Channel: Analytical, Empirical, and Computational Insights*

The focus of this study is on business-to-consumer (B2C) online auctions made possible by the advent of electronic commerce over an open-source, ubiquitous Internet Protocol (IP) computer network. This work presents an analytical model that characterizes the revenue generation process for a popular B2C online auction, namely, Yankee auctions. Such auctions sell multiple identical units of a good to multiple buyers using an ascending and open auction mechanism. The methodologies used to validate the analytical model range from empirical analysis to simulation. A key contribution of this study is the design of a partitioning scheme of the discrete valuation space of the bidders such that equilibrium points with higher revenue structures become identifiable and feasible. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of key control factors such as the bid increment, resulting in substantial losses in a market with already tight margins. With this in mind, we put forward a portfolio of tools, varying in their level of abstraction and information intensity requirements, which help auctioneers maximize their revenues.

Confidentiality via Camouflage: The CVC Approach to Disclosure Limitation When Answering Queries to Databases

A practical method is presented for giving unlimited, deterministically correct, numerical responses to ad-hoc queries to an online database, while not compromising confidential numerical data. The method is appropriate for any size database, and no assumptions are needed about the statistical distribution of the confidential data. Responses are in the form of a number plus a guarantee, so the user can determine an interval that is sure to contain the exact answer. Virtually any imaginable query type can be answered, and in the absence of insider information, collusion among the users presents no problem. Experimental analysis supports the practical viability of the proposed method.

Comparative Analysis of Multi-item Online Auctions: Evidence from the Laboratory *

Abstract The dynamics of customer relationship are being reshaped by price-setting processes such as online auctions. This paper analyzes price setting process in business-to-consumer (B2C) online auctions. Typically, these auctions involve multiple identical units and utilize a variant of the traditional English-auction mechanism. We describe an online laboratory experiment that compares the efficiency of such a mechanism with a multi-item version of Vickreys [Journal of Finance 41 (1961) 8.] second-price auction with respect to both sellers revenue and allocative efficiency. Our results reject the revenue equivalence principle and indicate that English auctions may dominate the Vickrey auctions. However, we observe that the allocative efficiency of Vickrey auctions is higher than the English auctions.

Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth

Enabled by Web 2.0 technologies social media provide an unparalleled platform for consumers to share their product experiences and opinions---through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics thereof are related to consumer purchases and product sales. By integrating network analysis with text sentiment mining techniques, we propose product comparison networks as a novel construct, computed from consumer product reviews. To test the validity of these product ranking measures, we conduct an empirical study based on a digital camera dataset from Amazon.com. The results demonstrate significant linkage between network-based measures and product sales, which is not fully captured by existing review measures such as numerical ratings. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that WOM in social media also constitutes a competitive landscape for firms to understand and manipulate.

Interval Protection of Confidential Information in a Database

We deal with the question of how to maintain security of confidential information in a database while answering as many queries as possible. The database is assumed to operate in a query restriction (as opposed to perturbation) mode in which exact answers are given to those queries which, together with those already answered, will not compromise any confidential datum. Those which fail this criterion are not answered. We introduce the concept of interval disclosure where a datum is compromised if the answered queries provide enough information to establish that it is contained in a given interval even if the datum cannot be determined exactly. Models are presented for the problem of deciding whether to answer a query and three techniques, one based on linear programming, are developed and tested.