This report is the result of a NSF-funded workshop that brought 33 social and computer scientists together to examine the dimensions of the social impact of the National Information Infrastructure and to ask what useful, critical and researchable questions the NII raises for society.

One of the promises of the information revolution was that it would increase the opportunities for all people to share knowledge. But what if you don't happen to live in a developed country? Subbiah Arunachalam argues that the current lack of access to the internet for scientists in the developing world is creating a new form of poverty - information poverty - which is making it harder for them to stay abreast, let alone catch up with their colleagues in the developed world.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Introduction to Special Issue of JASIS on AI Techniques for Emerging Information Systems Applications in which five articles report research in adopting artificial intelligence techniques for emerging information systems applications.

Various phenomena within the information systems discipline can be studied using the social network paradigm that views social entities as nodes with links between them. The social network analysis (SNA) theory has applications in knowledge management, computer mediated communications, security informatics, and other domains. Challenges in SNA can be classified into three broad areas: instance modeling, evolution modeling, and predictive modeling. Instance modeling focuses on the study of static network properties, evolution modeling examines factors behind network growth, and predictive modeling is concerned with identification of hidden and future network links. This dissertation presents four essays that address these challenges with empirical studies in knowledge management and security informatics.The first essay on instance and evolution modeling contributes to SNA theory by examining a real-world network that contains interactions between thirty thousand individuals. The study is among the few that empirically examine large human-only networks and verify the presence of small-world properties and scale-free distributions. In addition, it proposes a novel application of a network evolution model to examine the growth of networks across geographical boundaries.The second essay on evolution modeling proposes a methodology to identify significant link-formation facilitators. The study found that homophily in age, gender, and race were not significant factors in predicting future links between individuals in dark networks. These results contradicted some previous studies in the same domain that used smaller datasets to study the phenomena.The third essay focuses on evolution and predictive modeling and examines the role of inventor status on the selection of knowledge recombined to produce innovation. A new network measure based on random walks and team identification (RWT) is proposed to model knowledge flow. It is found that inventor status as measured by RWT has a positive relationship with the likelihood of a future citation link to the inventor.The fourth essay focuses on predictive modeling. A modified mutual information formulation is proposed to identify hidden links between nodes based on heuristics of time and location of previous co-occurrences. An evaluation of the proposed technique showed that it performed better in predicting hidden links than other co-occurrence based methods.

An automatic deception detection system (ADDS) is to detect deceptive human behavior with machine extractable evidences (i.e., cues). One of the most prominent challenges for building a ADDS is the availability of reliable cues. This study represents one of the first attempts to address the system's reliability by identifying the set of reliable cues in order to improve the system performance (detection accuracy).This study addresses two critical challenges of existing machine cues, irreproducibility and inconsistency. First, in order to mitigate the irreproducibility, the study introduces a set of machine measurable cues to estimate the commonality of related machine cues. These more reproducible cues are referred to as the macro cues which can be applied for automatic pattern recognition. Second, in order to address the consistency, the study separates cues based on the controllability, and defines the strategic cues as those can easily be manipulated by deceivers during interaction. The strategic cues fluctuate during deception and thus are less consistently reliable as predictors for the ADDS. On the contrary, the nonstrategic cues are more consistent. This study also considers other moderator effects that influencing the ADDS performance: time and the condition of interviewer's immediacy (ERIMD).The macro cues are automatically estimated from the micro cues based on the predefined relational models. The empirical data support the relationship models between macro and micro cues. Results show that macro cues mitigate the irreproducibility problem by reducing the variability in the single cues. However, the results also show that using macro cues as predictors in the discriminant analysis does not perform better than micro cues, and thus imply the needs to adjust weights of important components when constructing the macro cues. In terms of the consistent cues, results show that the nonstrategic cues are relatively more consistent than strategic ones in ADDS performance. Furthermore, the study suggests that particular detection methods must be tailored according to the feature of strategic and nonstrategic cues. The findings have many potential implications. One is to use the macro cues to recognize the dynamic patterns in deceptive behaviors. Specifically, truthtellers increase the certainty, immediacy, and tend to decrease the cognitive load; but deceivers behave the opposite. The other is to rely on the characteristics of strategic cues to manipulate the communication environment to improve the ADDS performance. This concept is also referred to as the Proactive Deception Detection (PDD). In the current study, the interviewer's immediacy is a controllable environment factor for PDD. The high ERIMD increase the system performance because it has higher overhead added to the deceptive behavior to trigger more abnormal cues. In sum, methods and results of this study have multiple impacts in information assurance and human-computer interaction.

Previous studies have explored self-verification theory to explain the relationship between self-verification and relationship conflict in collocated teams. This study expanded the application of self-verification theory in distributed team environments. More specifically, this study investigated the effects of similarity of personal identities on self-verification and the effects of self-verification on relationship conflict in both collocated and distributed teams. In addition to the self-verification theory, other identity related theories and communication theories were used to develop the hypotheses and to analyze the results. The hypotheses subsequently were tested using Partial Least Squares models. Participants of this study were members of one of the collocated or distributed engineering design teams. The findings show evidence of the moderation of team type on the relationship between self-verification and relationship conflict. The findings suggest that similarity of personal identities positively affects self-verification in collocated teams but not in distributed teams. Self-verification, in turn, reduces relationship conflict in collocated teams and increases relationship conflict in distributed teams. The implications of the findings on the theories, especially self-verification theory, are discussed.

This dissertation studies how information technologies, such as automatic question answering (QA), can add interactivity into a multimedia-based e-learning system and turn it into a "virtual mentor" to provide students with interactive, one-on-one instruction. It also explores the key factors of making such a "virtual mentor" as effective as a real mentor.Based on a review of multiple learning theories and technologies, an exploratory model for studying the effectiveness of interactive e-learning, named "Learning with Virtual Mentors (LVM)", is proposed and a prototype system is developed to implement the LVM model. A series of studies, including controlled experiments and surveys have been conducted to explore the relationships among the core constructs of the LVM model: learning phases, system interactivity, learner characteristics, learning activity and learning outcomes.Findings indicate that learning phases and some learner characteristics such as learning style affect students' behaviors, performance, and perceptions in e-learningpartly as we expected. Furthermore, the virtual interaction impacts student behaviors, encouraging students to interact more and increasing student satisfaction with the learning process. However, the correlation between virtual interaction and actual learning performance is limited. Consequently, the LVM model needs to be further explored and developed.

This research takes as its starting point the past decade of critiques from human geographers, and proposes an alternate model for appraisals of technology. The first section begins with an analysis of the bases and motivation of external assessments of GIS. A historiographical account reveals that the critical impulse among human geographers was not static, but evolved to incorporate greater subtlety based on cooperation with GIS scholars. Critiques from human geographers, nevertheless, had a profound impact on the discipline, and practitioners of GIS frequently felt that their perspectives on issues including the roots of GIS, its epistemological bases, and its ethics had been undervalued by critics. A re-analysis of critiques, from the perspectives of GIS practitioners, investigates objections to critical accounts of the technology. The second half of the research builds upon existing critiques and responses to them, but asks the question, "is there a more constructive means to engage with technology, from a theoretical perspective?" Two contemporary research questions in GIS are investigated, as a means of establishing a preliminary methodology for critique that engages with GIS at a conceptual, as well as a technical level. Factors that have influenced the progress of automated generalization are examined in some detail. The argument is made that both social and digital parameters define the technology, and it is unproductive to focus on one at the expense of the other. The second research question concerns data models and the extent to which fields and objects are inevitable. The case is made that a web of historical and scientific justification has prevented researchers from seeking alternatives to the atomic and plenum views of space. Finally, an appeal is made for continued theoretical examination of the technology as part of an effort to develop geographic information science.

More and more organizations are starting to use workflow management systems (WfMS) to monitor, control and manage business processes. However, currently available commercial workflow systems are rather rigid and cannot meet the requirements of a dynamic and fast-changing business. Exception handling capabilities of the systems are very limited. Some research work has been done to address the issue by extending database technologies in workflow domain. In this thesis, we begin with a brief review of some main workflow concepts and do a survey of current research work on exception handling. We propose a leveled workflow model based on Micro-Organization Activity Processor (MOAP) and Object-Oriented Workflow Model (OOWM), which is an extension of Object-Oriented Enterprise Modeling (OOEM). The MOAP construct is extended with a goal concept and the OOEM service concept. We then propose a mechanism for exception handling which utilizes artificial intelligence technologies such as means-end analysis. We further demonstrate the functionalities and exception handling processes with a web-based simulator by applying some workflow exception cases.

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