Price Premiums and Buyer Behavior Despite the wide use of reputational mechanisms such as eBay’s Feedback Forum to promote trust, empirical evidence has shown conflicting results on whether online feedback mechanisms really induce trust and lead to higher auction prices. This study examines the extent to which trust can be induced by proper feedback mechanisms in electronic markets, and how some risk factors play a role in trust formation. Drawing from economic, sociological, and marketing theories and using data from both an online experiment and an online auction market, we demonstrate that appropriate feedback mechanisms can induce calculus-based credibility trust without repeated interactions between two transacting parties. Trust can mitigate information asymmetry by reducing transaction-specific risks, therefore generating price premiums for reputable sellers. In addition, the research also examines the role that trust plays in mitigating the risks in transactions that involve very expensive products or experience products.

Media access control (MAC) address spoofing can result in the unauthorized use of network resources. This paper demonstrates a novel approach, which incorporates radio frequency fingerprinting (RFF) into a wireless intrusion detection system (IDS), for detecting this attack. RFF is a technique that is used to uniquely identify a transceiver based on the transient portion of the signal it generates. Moreover, the success rate of a wireless IDS is also improved by correlating several observations in time, using a Bayesian filter. Simulation results, with an average success rate of (94-100%), support the feasibility of employing RFF and Bayesian filtering techniques to successfully address the aforementioned problem.

This research examines the impact of primitive affective reactions to information technology on subsequent cognitive reactions and behavioral intention on IT use, and the potential change of such impact over time. We ground our work in theories of psychology and information systems and propose a theoretical model in which the user’s perceptions regarding the affective quality of an IT influences cognitive reactions and behavioral intention to use IT. The model was validated by surveys in two field studies of 226 and 196 college students, respectively, who were asked to evaluate a course management system, WebCT. The first study occurred during weeks 3 and 4 of the spring 2004 semester, when subjects were getting familiar with WebCT for their classes. The second study ran during weeks 11 and 12 of the same semester, when WebCT had been used quite intensively in the classes. The theoretical model is supported by both studies, indicating that the impact of perceived affective quality persists, even when subjects ’ familiarity with and use of the IT increases. Our research identifies perceived affective quality as another, more-fundamental, and sustained source of user intention of IT use that has not been widely recognized yet. From a theoretical perspective, this research breaks the conventional cognition-driven paradigm of studying user reactions to technology and calls for attention to affect and emotion in examining people’s everyday, normal interactions with IT. Practically, the research provides empirical evidence for IT designers, trainers, and stakeholders to better strategize their resources and emphases.

In this paper, a new method is illustrated to cluster navigation patterns on a website. Instead of clustering users by means of a Euclidean distance measure, in our approach users are partitioned into clusters using a Sequence Alignment Method. This method ensures that sequential relationships, which are captured in the data, are taken into account. The performance of the algorithm is compared with the results of a method based on Euclidean distance measures. The proposed method is validated using usertraffic data from a Belgian telecom provider. Empirical results show that the method extracts sequences with similar behavioural patterns not only with regard to content but also considering the order of pages that are visited in a sequence. 1

We are focusing on three alternative techniques that can be used to empirically select predictors for failure prediction purposes. The selected techniques have all different assumptions about the relationships between the independent variables. Linear discriminant analysis is based on linear combination of independent variables, logit analysis uses the logistic cumulative probability function and genetic algorithms is a global search procedure based on the mechanics of natural selection and natural genetics. Our aim is to study if these essential differences between the methods (1) affect the empirical selection of independent variables to the model and (2) lead to significant differences in failure prediction accuracy.

Efficient, flexible, and scalable integration of full text information retrieval (IR) in a DBMS is not a trivial case. This holds in particular for query optimization in such a context.

Experimental subjects evaluated a candidate for local office whose face was digitally altered to absorb the subjects ’ facial structure. For half of the subjects, the photograph of the candidate was morphed such that the image presented was a blend composed of 60 % of the unfamiliar Caucasian male and 40 % of the subject. For the other half the photograph was unaltered. Given previous research on implicit familiarity (Zajonc, 1968, 1980), we predicted that the morph would advantage the candidate. The results demonstrated no main effect of the similarity manipulation. However, there was a robust similarity by gender interaction. Male subjects evaluated the morphed candidate more favorably than the unaltered photograph, while female subjects rated him more negatively. We discuss potential explanations for this interaction effect, and explore the possible implications of facial similarity as a political cue in both high- and low-information

Abstract — For the purpose of anomaly-based intrusion detection in mobile networks, the utilization of profiles based on hardware signatures, calling patterns, service usage and mobility patterns have been explored by various research teams and commercial systems, namely the Fraud Management System by Hewlett-Packard and Compaq. This paper examines the feasibility of using profiles, which are based on the mobility patterns of mobile users, who make use of public transportation, e.g. bus. More specifically, a novel framework, which makes use of an instance based learning technique, for classification purposes, is presented. In addition, an empirical analysis is conducted in order to assess the impact of two key parameters, namely the sequence length and precision level, on the false alarm and detection rates. Moreover, a strategy for enhancing the characterization of users is also proposed. Based on simulation results, it is feasible to use mobility profiles to enhance anomalybased intrusion detection in mobile wireless networks, provided that the user mobility profiles adequately reflect the mobility behavior of users.

Perceptual consistency is important in many computer vision applications. Unfortunately, except for color, computational features and similarity measurements for other visual features are not necessarily consistent with human's perception. This paper addresses three critical issues regarding perceptually consistent texture analysis: (1) development of perceptual texture space, (2) assessment of how consistent computational features are to human perception, and (3) mapping computational features to perceptual space. It demonstrates the construction of a reliable perceptual texture space, which can be used as a yardstick for assessing the perceptual consistency of computational features and similarity measurements. Moreover, it is found that commonly used computational texture features are not very consistent with human perception, and mapping them to the perceptual space improves their perceptual consistency. 1

Decision Support systems The increasingly global nature of software development and the increasing diversity in the information systems (IS) workforce have led to failures arising from inadequate understanding of cultural influences on IS design and development. This study aims to examine the relationship between the cultural background and development values of IS developers through a field survey in Singapore and the United States. The relationship between two cultural dimensions (individualism-collectivism and masculinity-femininity) and three development values (technical, economic, and socio-political) of IS developers were investigated. A model relating cultural dimensions to development values was formulated. The findings indicate that both individualism-collectivism and masculinity-femininity are related to the technical, economic, and socio-political values of IS developers (including DSS developers). Implications for further research and practice are discussed.