Contributions published at Business Intelligence (Daning Hu)

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The 2008 banking crisis has demonstrated that there is the lack of effective methods for modeling and analyzing “exceptional but plausible” risk scenarios in bank stress testing. However, existing bank stress testing practices mainly focus on modeling probability-based risk factors and events in a “static snapshot” of the banking systems, but largely ignore the dynamic processes in which financial crisis events and their interactions creates various complex risk scenarios. In addition, the rare (low probability) risk events such as the bankruptcy of Lehman Brothers that can cause “exceptional but plausible” crisis scenarios are largely ignored due to the lack of appropriate modeling and analysis methods. To address this problem, we developed an approach called Banking Event-driven Scenario-oriented Stress Testing (or simply the BESST) which mainly includes three components: 1) a set of stress testing ontologies; 2) an event-driven scenario model (OESM); and 3) a scenario recommendation component. In addition, we show how to use BESST to model and examine “exceptional but plausible” stress testing scenarios in an example process of crisis events. In general, this research has provided the bank stress testing stakeholders a novel approach for modeling and analyzing the rare risk events and their dynamic processes in various financial crisis scenarios.

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In recent years there is a growing trend in business firms that aims to transform their manufacturing-based business models, processes and practices to service-based ones. This trend is especially important for Information Technology (IT) industry since IT is considered as a major provider of various business services. However, there is little empirical research on the service relationships in the IT industry level. We adopt a network perspective to empirically investigate a financial dataset that contains service-related transactions between 587 IT companies and their major clients. It was found that the constructed customer-supplier network in IT industry shows scale-free network topology. This finding indicates that IT service market is dominated by a few large customers such as U.S. Government and industry leaders. Leading IT companies such as DELL and Microsoft are more of service customers than providers. The results of our descriptive analysis may provide potential research directions for empirical service researchers and practical implications for IT service firms and professions.

In the wake of the 2008 financial tsunami, existing methods and tools for managing financial risks have been criticizedfor weaknesses in monitoring and alleviating risks at the systemic level. A 2009 article in Nature suggested newapproaches to modeling economic meltdowns are needed to prevent future financial crises. However, existing studieshave not focused on analysis of systemic risk at the individual bank level in a banking network, which is essential formonitoring and mitigating contagious bank failures. To this end, we develop a network approach to risk management(NARM) for modeling and analyzing systemic risk in banking systems. NARM views banks as a network that is linkedthrough financial relationships. It incorporates network and financial principles into a business intelligence (BI)algorithm to analyze systemic risk attributed to each individual bank via simulations based on real-world data from theFederal Deposit Insurance Corporation. Our research demonstrates the feasibility of modeling and analyzing systemicrisk at the individual bank level in a banking network using a BI-based approach. In terms of business impacts, NARMoffers a new means for predicting contagious bank failures and determining capital injection priorities in the wake offinancial crises. Our simulation study shows that under significant market shocks, the interbank payment relationshipbecomes more influential than the correlated bank portfolio relationship in determining an individual bank’s survival. These insights should help financial regulators devise more effective policies and mechanisms to prevent the collapseof a banking system. Further, NARM and the simulation procedure driven by real-world data proposed in this study haveinstructional value to similar research areas such as bank stress testing, where time series data and business networksmay be studied.

Successful development of open source software (OSS) projects requires a steady supply of self motivated software developers. Thus a large body of OSS studies focuses on studying the developers' participation motivations. One important motivation is an OSS developer's desire to gain good community reputation which is largely based on positive evaluations from his peers. Therefore, to better motivate developers' project participations through their reputation needs, our empirical analysis adopted a social network perspective to study what factors may affect a developer's decision to positively evaluate one other in a large online open source community called Ohloh. The results surprisingly show that a developer's positive evaluation decision does not depend on his evaluatee's level of OSS-related experience, but rather based on 1) his past reputation (i.e. existing number of positive evaluations), 2) their shared affiliations such as mutual acquaintances, as well as 3) their homophily in location (city), nationality, programming language preference, and community status. We then discuss these findings and their implications for inducing more positive evaluations and better reputation management among open source project members. To the best of our knowledge, this paper is the first research that investigates issues of reputation building and relationship management in an open source development context.

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches.

As cooperation in a networked manner increases via various inter-organizational information systems (IOISs), it is important to choose appropriate IOISs for different types of organizations in the network environment. In this study, we analyzed customer-supplier relationships among organizations in five industries using social network analysis (SNA) methods and empirical data, aiming to help organizations strategically choose appropriate IOISs. Three types of customer-supplier networks were identified based on the network centralization comparison rate: customer-centric, supplier-centric and balanced networks. Based on the empirical findings in our analysis, we then propose strategies about how to choose appropriate IOISs for the firms in these networks and discuss the pros and cons of the choices. To the best of our knowledge, this is the first empirical research that applied SNA methods to study customer-supplier networks in the context of inter-organizational information systems.

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Nanotechnology research has experienced rapid growth in recent years. Advances in information technology enable efficient investigation of publications, their contents, and relationships for large sets of nanotechnology-related documents in order to assess the status of the field. This paper presents the development of a new knowledge mapping system, called Nano Mapper ( http://nanomapper.eller.arizona.edu ), which integrates the analysis of nanotechnology patents and research grants into a Web-based platform. The Nano Mapper system currently contains nanotechnology-related patents for 1976–2006 from the United States Patent and Trademark Office (USPTO), European Patent Office (EPO), and Japan Patent Office (JPO), as well as grant documents from the U.S. National Science Foundation (NSF) for the same time period. The system provides complex search functionalities, and makes available a set of analysis and visualization tools (statistics, trend graphs, citation networks, and content maps) that can be applied to different levels of analytical units (countries, institutions, technical fields) and for different time intervals. The paper shows important nanotechnology patenting activities at USPTO for 2005–2006 identified through the Nano Mapper system.

Today terrorists usually work in network forms to conduct attacks. Terrorist networks remain active and can still function even after being severely damaged by authorities. Analyzing terrorist networks from a dynamic point of view can provide insights about the mechanisms responsible for the survival of terrorist organizations. This paper studies the dynamics of a major international terrorist organization over a 14-year period – the Global Salafi Jihad (GSJ) terrorist network. We found that a scale-free topology gradually emerged as new members joined the GSJ network based on operational needs. In addition, since the network has been experiencing member losses while it grows, we also studied the robustness of the GSJ network. We used a simulation approach to examine its vulnerability to random failures, targeted attacks, and real world authorities' counterattacks. We found that authorities' counterattacks have been rather ineffective in disrupting the terrorist network.

Social networks evolve over time with the addition and removal of nodes and links to survive and thrive in their environments. Previous studies have shown that the link-formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the important facilitators. In a research partnership with law enforcement agencies, we used dynamic social-network analysis methods to examine several plausible facilitators of co-offending relationships in a large-scale narcotics network consisting of individuals and vehicles. Multivariate Cox regression and a two-proportion z-test on cyclic and focal closures of the network showed that mutual acquaintance and vehicle affiliations were significant facilitators for the network under study. We also found that homophily with respect to age, race, and gender were not good predictors of future link formation in these networks. Moreover, we examined the social causes and policy implications for the significance and insignificance of various facilitators including common jails on future co-offending. These findings provide important insights into the link-formation processes and the resilience of social networks. In addition, they can be used to aid in the prediction of future links. The methods described can also help in understanding the driving forces behind the formation and evolution of social networks facilitated by mobile and Web technologies.

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