http://hdl.handle.net/1903/1584 2013-05-22T00:47:18Z http://hdl.handle.net/1903/13874 Title: Technical Appendix for "Media, Aggregators, and the Link Economy: Strategic Hyperlink Formation in Content Networks" Authors: Dellarocas, Chrysanthos; Katona, Zsolt; Rand, William 2013-01-01T00:00:00Z http://hdl.handle.net/1903/13592 Title: LEARNING MECHANSIMS AND HEALTH INFORMATION TECHNOLOGY Authors: Kothari, Anupama Kunal Abstract: Health information technology (HIT) is gaining momentum and widespread use globally in healthcare institutions through the implementation and use of HIT such as telemedicine and electronic medical records. Literature has discussed various aspects of health information technology such as increasing the accessibility of healthcare, improving the efficacy and reducing associated errors. However, the potential of HIT as a medium of learning has largely been ignored by extant literature. This dissertation seeks to understand the mechanisms of learning in the context of health information technologies, specifically- telemedicine and electronic medical records. The two essays investigate the characteristics of learning under telemedicine and under electronic records. The first essay uses a quantitative mode of investigation, while the second essay utilizes a qualitative mode of research. The first essay deals with telemedicine, a healthcare information technology that provides healthcare across geographic boundaries. The essay investigates how the telemedicine process facilitates synchronous learning in terms of a facilitator-learner theoretical model. It explores the impact of facilitator characteristics and learner characteristics on synchronous learning. Additionally, the essay also examines the impact of organizational variables such as technology on the relationship between learning and facilitator-learner mechanics. Data for this essay is drawn from surveys administered over several hospitals that use telemedicine in India. The second essay studies the role of electronic medical records in information dissemination and learning. In this essay, the role of electronic medical records in providing healthcare personnel with asynchronous learning opportunities is investigated. It explores the impact of individual and organizational factors on discovery learning through electronic medical records. The essay identifies factors such as case complexity, status, familiarity with technology and clinical specialty that influence learning through electronic medical records. The second essay draws on interviews of members of a healthcare team in a multiple specialty hospital that uses electronic medical records. Together, the essays explore various aspects of learning through health information technology, including synchronous learning, asynschronous learning, learning mechanics and motivations for learning. 2012-01-01T00:00:00Z http://hdl.handle.net/1903/13103 Title: Integrating Social Network Effects in Product Design and Diffusion Authors: Gunnec, Dilek Abstract: Connectivities among people are amplified with recent advancements in internet technology increasing the number of communication channels. Information spread over these networks strengthen the social influence among individuals and affect their purchasing decisions. In this thesis, we study three problems in the product design and diffusion context by integrating such social network effects where influence takes place over neighborhood relationship ties among the users of the product. We consider the setting where peer influence plays a significant role in a consumer's product choice or there is a tangible benefit from using the same product as the rest of one's social network. Building upon the well-known Share-of-Choice problem, we model an influence structure and define the Share-of-Choice problem with Network Effects. It is an NP-Hard combinatorial optimization problem which we solve using a Genetic Algorithm. Using simulated data we show that ignoring social network effects in the design phase of a product results in a significantly lower market share for a product. Our genetic algorithm obtains near-optimal solutions and is very robust in terms of its running time, scalability, and ability to adapt to additional constraints/variations of the model. In this setting, we introduce a product diffusion problem, the Least Cost Influence Problem, which increases the market share of a product by intervening the natural diffusion of it over the social network. This intervention is in the form of incentive supply to a group of people in a least costly way while maximizing the spread of the product. We generalize the Least Cost Influence Problem by moving away from the marketing setting and by treating the previous product as any piece of "information" that can spread over a social network by adoption. We show that this problem is polynomially solvable over tree networks under some conditions. We provide a Dynamic Programming algorithm to solve this problem and show that it can be interpreted as a greedy algorithm that gives incentives starting with the people that are least influenced by their neighbors, albeit the definition of susceptibility to influence from neighbors is updated throughout the algorithm. We introduce a two dimensional influence model and extend our modeling and solution methods for the product line design problem which involves designing multiple products within the same product line with the objective of appealing to the heterogeneous structure of the market. The first dimension of influence is the affection of individuals from using the same product, and the second dimension is the influence of using a similar product from the same product line which has a lower intensity of influence. We reexamine the Least Cost Influence Problem in the product line setting. 2012-01-01T00:00:00Z http://hdl.handle.net/1903/13098 Title: MODELS AND SOLUTION ALGORITHMS FOR EQUITABLE RESOURCE ALLOCATION IN AIR TRAFFIC FLOW MANAGEMENT Authors: Zhong, Ming Abstract: Population growth and economic development lead to increasing demand for travel and pose mobility challenges on capacity-limited air traffic networks. The U.S. National Airspace System (NAS) has been operated near the capacity, and air traffic congestion is expected to remain as a top concern for the related system operators, passengers and airlines. This dissertation develops a number of model reformulations and efficient solution algorithms to address resource allocation problems in air traffic flow management, while explicitly accounting for equitable objectives in order to encourage further collaborations by different stakeholders. This dissertation first develops a bi-criteria optimization model to offload excess demand from different competing airlines in the congested airspace when the predicted traffic demand is higher than available capacity. Computationally efficient network flow models with side constraints are developed and extensively tested using datasets obtained from the Enhanced Traffic Management System (ETMS) database (now known as the Traffic Flow Management System). Representative Pareto-optimal tradeoff frontiers are consequently generated to allow decision-makers to identify best-compromising solutions based on relative weights and systematical considerations of both efficiency and equity. This dissertation further models and solves an integrated flight re-routing problem on an airspace network. Given a network of airspace sectors with a set of waypoint entries and a set of flights belonging to different air carriers, the optimization model aims to minimize the total flight travel time subject to a set of flight routing equity, operational and safety requirements. A time-dependent network flow programming formulation is proposed with stochastic sector capacities and rerouting equity for each air carrier as side constraints. A Lagrangian relaxation based method is used to dualize these constraints and decompose the original complex problem into a sequence of single flight rerouting/scheduling problems. Finally, within a multi-objective utility maximization framework, the dissertation proposes several practically useful heuristic algorithms for the long-term airport slot assignment problem. Alternative models are constructed to decompose the complex model into a series of hourly assignment sub-problems. A new paired assignment heuristic algorithm is developed to adapt the round robin scheduling principle for improving fairness measures across different airlines. Computational results are presented to show the strength of each proposed modeling approach. 2012-01-01T00:00:00Z