Link formation in mobile and economic networks : model and empirical analysis

In this dissertation, we study three link formation problems in mobile and economic networks: (i) company matching for mergers and acquisitions (M&A) network in the high-technology (high-tech) industry, (ii) mobile application (app) matching for cross promotion network in mobile app markets, and (iii) online friendship formation in mobile social networks. Each problem can be modeled as link formation problem in a graph, where nodes represent independent entities (e.g., companies, apps, users) and edges represent interactions (e.g., transactions, promotions, friendships) among the nodes. First, we propose a new data-analytic approach to measure firms' dyadic business proximity to analyze M&A network in the high-tech industry. Specifically, our method analyzes the unstructured texts that describe firms' businesses using latent Dirichlet allocation (LDA) topic modeling, and constructs a novel business proximity measure based on the output. Using CrunchBase data including 24,382 high-tech companies and 1,689 M&A transactions, we empirically validate our business proximity measure in the context of industry intelligence and show the measure's effectiveness in an application of M&A network analysis. Based on the research, we build a cloud-based information system to facilitate competitive intelligence on the high-tech industry. Second, we analyze mobile app matching for cross promotion network in mobile app markets. Cross promotion (CP) is a new app promotion framework, in which a mobile app is promoted to the users of another app. Using IGAWorks data covering 1,011 CP campaigns, 325 apps, and 301,183 users, we evaluate the effectiveness of CP campaigns in comparison with existing ad channels such as mobile display ads. While CP campaigns, on average, are still suboptimal as compared with display ads, we find evidence that a careful matching of mobile apps can significantly improve the effectiveness of CP campaigns. Our empirical results show that app similarity, measured by LDA from apps' text descriptions, is a significant factor that increases the user engagement in CP campaigns. With this observation, we propose an app matching mechanism for the CP network to improve the ad effectiveness. Third, we study friendship network formation in a location-based social network. We build a structural model of social link creation that incorporates individual characteristics and pairwise user similarities. Specifically, we define four user proximity measures from biography, geography, mobility, and short messages (i.e., tweets). To construct proximity from unstructured text information, we build LDA topic models of user biography texts and tweets. Using Gowalla data with 385,306 users, three million locations, and 35 million check-in records, we empirically estimate the structural model to find evidence on the homophily effect in network formation.Computer Science

Learning Faces to Predict Matching Probability in an Online Matching Platform

With the increasing use of online matching platforms, predicting matching probability between users is crucial for efficient market design. Although previous studies have constructed various visual features to predict matching probability, facial features, which are important in online matching, have not been widely used. We find that deep learning-enabled facial features can significantly enhance the prediction accuracy of a user’s partner preferences from the individual rating prediction analysis in an online dating market. We also build prediction models for each gender and use prior theories to explain different contributing factors of the models. Furthermore, we propose a novel method to visually interpret facial features using the generative adversarial network (GAN). Our work contributes the literature by providing a framework to develop and interpret facial features to investigate underlying mechanisms in online matching markets. Moreover, matching platforms can predict matching probability more accurately for better market design and recommender systems

A Scaling Perspective on AI Startups

Digital startups’ use of AI technologies has significantly increased in recent years, bringing to the fore specific barriers to deployment, use, and extraction of business value from AI. Utilizing a quantitative framework regarding the themes of startup growth and scaling, we examine the scaling behavior of AI, platform, and service startups. We find evidence of a sublinear scaling ratio of revenue to age-discounted employment count. The results suggest that revenue-employee growth pattern of AI startups is close to that of service startups, and less so to that of platform startups. Furthermore, we find a superlinear growth pattern of acquired funding in relation to the employment size that is largest for AI startups, possibly suggesting hype tendencies around AI startups. We discuss implications in the light of new economies of scale and scope of AI startups related to decision-making and prediction

On the Heterogeneity of Digital Infrastructure in Entrepreneurial Ecosystems

Digital infrastructure represents for startups in entrepreneurial ecosystems an important asset but also a major risk. Drawing on studies about digital entrepreneurship and ecosystems, we examine the determinants of the heterogeneity of startups’ tech stacks in ecosystems. Using publicly available data from the data aggregators Stackshare and Crunchbase, we identify popular endogenous categories in startups’ tech stacks. Then we conduct a visual network analysis and a multivariate regression analysis, utilizing the identified technology categories to measure the heterogeneity of the startups’ tech stacks. The analysis supports the propositions that firm age and increased funding are positively associated with tech stack heterogeneity, whereas funding rounds are negatively associated with tech stack heterogeneity. Implications of our findings on digital entrepreneurship and ecosystems are discussed

IT Risk Factor Disclosure and Stock Price Crashes

As firms are increasingly more dependent on Information Technology (IT) for their business strategies and value creation activities, risks associated with IT become one of the top concerns for corporate boards and managers. This study examines the impact of IT-related risk factor disclosure in Item 1A of the 10-K annual report on stock price crashes. We use Latent Dirichlet Allocation topic modeling to identify risk categories in risk disclosures between 2006 and 2017. IT risk emerged as one of the key risk categories. We find that IT risk disclosure is positively correlated with a firm’s future stock price crash risk. We further separate IT risk factor disclosures into two categories: IT value risk that relates to a firm’s use of and reliance on information technology for its operations to reach its goals and objectives, and cybersecurity risk that could lead to a loss or leak of data. We find that while the correlation between cyber security risk disclosure and a firm’s future crash risk is significant, IT value risk disclosures do not have a significant correlation

Information Disclosure and Security Vulnerability Awareness: A Large-Scale Randomized Field Experiment in Pan-Asia

This paper investigates how the disclosure of a security vulnerability index based on outgoing spams and phishing website hosting which may serve as an indicator of a firm’s inadequate security controls affects companies’ security protection strategy. Our core objective is to study whether firms improve their security when they become aware of their vulnerabilities and such information is publicized. To achieve this goal, we conduct a randomized field experiment on 1,262 firms in six Pan-Asian countries and regions. Among 631 treatment firms, we alert them of their security vulnerability index and ranking over time, and their relative performance compared to their peers via emails and a public advisory website. Compared with control firms without being informed of their security vulnerability index, treatment firms improve their security over time, with a significant reduction of outgoing spam volume. A marginally significant improvement in reducing phishing hosting websites is also observed among non-web hosting treatment firms. The security improvement may be attributed to firms’ proactive reaction to the public security vulnerability information. Our study provides cybersecurity policy makers with useful insights to motivate firms to adopt better security measures

Sublingual Immunization with M2-Based Vaccine Induces Broad Protective Immunity against Influenza

The ectodomain of matrix protein 2 (M2e) of influenza A virus is a rationale target antigen candidate for the development of a universal vaccine against influenza as M2e undergoes little sequence variation amongst human influenza A strains. Vaccine-induced M2e-specific antibodies (Abs) have been shown to display significant cross-protective activity in animal models. M2e-based vaccine constructs have been shown to be more protective when administered by the intranasal (i.n.) route than after parenteral injection. However, i.n. administration of vaccines poses rare but serious safety issues associated with retrograde passage of inhaled antigens and adjuvants through the olfactory epithelium. In this study, we examined whether the sublingual (s.l.) route could serve as a safe and effective alternative mucosal delivery route for administering a prototype M2e-based vaccine. The mechanism whereby s.l. immunization with M2e vaccine candidate induces broad protection against infection with different influenza virus subtypes was explored.A recombinant M2 protein with three tandem copies of the M2e (3M2eC) was expressed in Escherichia coli. Parenteral immunizations of mice with 3M2eC induced high levels of M2e-specific serum Abs but failed to provide complete protection against lethal challenge with influenza virus. In contrast, s.l. immunization with 3M2eC was superior for inducing protection in mice. In the latter animals, protection was associated with specific Ab responses in the lungs.The results demonstrate that s.l. immunization with 3M2eC vaccine induced airway mucosal immune responses along with broad cross-protective immunity to influenza. These findings may contribute to the understanding of the M2-based vaccine approach to control epidemic and pandemic influenza infections

On the interactions of overlay routing

Overlay routing has been successful as an incremental method to improve the current Internet routing by allowing users to select the Internet paths by themselves. By its nature, overlay routing has selfish behavior, which makes impact on the related components of the Internet routing. In this thesis, we study three interactions related to overlay routing. First, overlay routing changes the traffic patterns observed by the network operating side, which uses traffic engineering techniques to cope with the dynamic traffic demands. We improve this vertical interaction between overlay routing and traffic engineering. Secondly, the performance of overlay routing may be affected by the action of other coexisting overlays. An initial result on the horizontal interaction among multiple overlays is given. Lastly, within a single overlay network, overlay nodes can be regarded as independent decision makers, who act strategically to maximize individual gain. We design an incentive-based framework to achieve Pareto-optimality in the internal interaction of overlay routing.Computer Science