Information Systems and Health Care XII: Toward a Consumer-to-Healthcare Provider (C2H) Electronic Marketplace

Recent technological advances and the heightened expectations of e-health consumers are about to transform the U.S. health care industry. As consumers demand more online services, physicians respond by adopting information technology into their daily routines. In this paper, we first present recent developments within the telemedicine and e-health fields that necessitate the establishment of a consumer-to-healthcare provider (C2H) electronic marketplace. Next, we discuss the services this marketplace should offer to both consumers and physicians for it to thrive in this extensively regulated industry. Finally, we compare these services with those provided by what we call a first-generation C2H marketplace, a comparison that clearly outlines practical implications for the C2H marketplaces of the future

On the Formation of Peer-to-Peer Networks: Self-Organized Sharing and Groups

In this paper, we investigate the formation of peer-to-peer (P2P) networks with rational participating agents (active peers). In the absence of a central planner, peers choose their own utility-maximizing strategies for coalition and peer formation. P2P networks evolve dynamically through the activities of interactions among individual nodes and group units. We propose a framework for multilevel formation dynamics, including an individual level (content sharing decision and group selection) and a group level (membership admission). The respective utilities of the individual node and the collective player are formulated as functions of operational performance metrics such as expected content availability, search delay, transmission delay, and download delay. We study the impacts of various system parameters on the emergence of self-organized P2P network configuration features such as free-riding level and group size. Furthermore, we investigate the stability and efficiency of P2P networks and propose internal transfer mechanisms that force stable networks to become efficient

Reconciling Attribute Values from Multiple Data Sources

Because of the heterogeneous nature of multiple data sources, data integration is often one of the most challenging tasks of today’s information systems. While the existing literature has focused on problems such as schema integration and entity identification, our current study attempts to answer a basic question: When an attribute value for a real-world entity is recorded differently in two databases, how should the “best” value be chosen from the set of possible values? We first show how probabilities for attribute values can be derived, and then propose a framework for deciding the cost-minimizing value based on the total cost of type I, type II, and misrepresentation errors

Continued participation in online innovation communities: Does community response matter equally for everyone?

In this study, we focus on the factors that influence online innovation community members\u27 continued participation in the context of open source software development (OSSD) communities. Prior research on continued participation in online communities has primarily focused on social interactions among members and benefits obtained from these interactions. However, members of these communities often play different roles, which have been examined extensively, albeit in a separate stream of research. This study attempts to bridge these two streams of research by investigating the joint influence of community response and members\u27 roles on continued participation. We categorize OSSD community members into users and modifiers and empirically examine the differential effects of community response across these roles. By analyzing a longitudinal data set of activities in the discussion forums of more than 300 OSSD projects, we not only confirm the positive influence of community response on members\u27 continued participation but also find that community response is more influential in driving the continuance behavior of users than that of modifiers. In addition, this research highlights the importance of modifiers, a key subgroup of OSSD participants that has been largely overlooked by prior research. © 2013, INFORMS

A Probabilistic Decision Model for Entity Matching in Heterogeneous Databases

In recent years, there has been a proliferation of database systems in all types of organizations. In many cases, these databases are developed in different departments and maintained autonomously. Much is to be gained, however, if databases across departments, divisions, or even organizations can be related to one another. One main problem of relating data stored in different databases is the differences in their representation of real-world entities, such as the use of different identifiers or primary keys. We present a decision theoretic model for matching entities across different databases. The decision to match two entities from two different databases inherently involves some uncertainty since an exact match may not be found because of errors in data collection, data entry, and data representation. We model this uncertainty using probability theory and propose an integer programming formulation that minimizes the total cost associated with the entity matching decision. The model has been implemented and validated on real-world data.Semantic Heterogeneity, Matching Under Uncertainty, Classification Costs, Assignment Problem

Technology Usage and Online Sales: An Empirical Study

Despite the widespread adoption of search and recommendation technologies on the Internet, empirical research that examines the effect of these technologies is scarce. How do online consumers use these technologies? Does consumers' technology usage have an effect on the sales to them or their purchasing patterns? This paper empirically measures consumers' usage of website technologies by analyzing server log data. We match technology usage data to sales data, controlling for consumers' historical purchasing behavior. Our unique data set allows us to reveal the relationship between technology usage and online sales. Our analyses show that consumers' information technology usage has a significant effect on the sales to them, but this effect varies for different technologies and across different products. In particular, the use of directed search has a positive effect on the sales of promoted products, whereas it has a negative effect on the sales of nonpromoted products. In contrast, the use of a recommendation system has a positive effect on the sales of both promoted and nonpromoted products. Surprisingly, the use of nondirected search has an insignificant effect on online sales.electronic commerce, Internet, technology usage, online sales, search, recommendation