Based on our interactions with the key personnel of three different healthcare information exchange (HIE) providers in Texas, we develop models to study the sustainability of HIEs and participation levels in these networks. We first examine how heterogeneity among healthcare practitioners (HPs) (in terms of their expected benefit from the HIE membership) affects participation of HPs in HIEs. We find that, under certain conditions, low-gain HPs choose not to join HIEs. Hence, we explore several measures that can encourage more participation in these networks and find that it might be beneficial to (i) establish a second HIE in the region, (ii) propose more value to the low-gain HPs, or (iii) offer or incentivize value-added services. We present several other interesting and useful results that are somewhat counterintuitive. For example, increasing the highest benefit the HPs can get from the HIE might decrease the number of HPs that want to join the HIE. Furthermore, since the amount of funds from the government and the other agencies often changes (and will eventually cease), we analyze how the changes in the benefit HPs obtain from the HIE affect (i) participation in the network, (ii) the HIE subscription fee and the fee for value-added service, (iii) the number of HPs that request value-added service, and (iv) the net values of the HIE provider and HPs. We also provide guidelines for policy makers and HIE providers that may help them improve the sustainability of HIEs and increase the participation levels in these networks.
We study operational and managerial problems arising in the context of security monitoring where sessions, rather than raw individual events, are monitored to prevent attacks. The objective of the monitoring problem is to maximize the benefit of monitoring minus the monitoring cost. The key trade-off in our model is that as more sessions are monitored, the attack costs should decrease. However, the monitoring cost would likely increase with the number of sessions being monitored. A key step in solving the problem is to derive the probability density of a system with n sessions being monitored with a session's age measured as the time elapsed since it last generated a suspicious event. We next optimize the number of sessions monitored by trading off the attack cost saved with the cost of monitoring. A profiling step is added prior to monitoring and a resulting two-dimensional optimization problem is studied. Through numerical simulation, we find that a simple size-based policy is quite robust for a very reasonable range of values and, under typical situations, performs almost as well as the two more sophisticated policies do. Also, we find that adopting a simplified policy without using the option of managing sessions using age threshold can greatly increase the ease of finding an optimal solution, and reduce operational overhead with little performance loss compared with a policy using such an option. The insights gained from the mechanics of profiling and monitoring are leveraged to suggest a socially optimal contract for outsourcing these activities in a reward-based contract. We also study penalty-based contracts. Such contracts (specifically, when the penalty is levied as a percentage of the monthly service fee) do not achieve the social optimum. We show how an appropriate penalty coefficient can be chosen to implement a socially optimal penalty-based contract. In addition, we provide a high-level comparison between reward- and penalty-based contracts. In a penalty-based contract, the setting of the fixed payment can be challenging because it requires additional knowledge of the total expected malicious event rate, which needs to be observed through a period of no monitoring.
How do recommender systems affect prices and profits of firms under competition? To explore this question, we model the strategic behavior of customers who make repeated purchases at two competing firms: one that provides personalized recommendations and another that does not. When a customer intends to purchase a product, she obtains recommendations from the personalizing firm and uses this recommendation to eventually purchase from one of the firms. The personalizing firm profiles the customer (based on past purchases) to recommend products. Hence, if a customer purchases less frequently from the personalizing firm, the recommendations made to her become less relevant. While considering the impact on the quality of recommendations received, a customer must balance two opposing forces: (1) the lower price charged by the non-personalizing firm, and (2) an additional fit cost incurred when purchasing from the non-personalizing firm and the increased cost due to recommendations of reduced quality in the future. An outcome of the analysis is that the customers should distribute their purchases across both firms to maximize surplus over a planning horizon. Anticipating this response, the firms simultaneously choose prices. We study the sensitivity of the equilibrium prices and profits of the firms with respect to the effectiveness of the recommender system and the profile deterioration rate. We also analyze some interesting variants of the base model in order to study how its key results could be influenced. One of the key takeaways of this research is that the recommender system can influence the price and profit of not only the personalizing firm but also the non-personalizing firm. > >
We consider advertising problems under an information technology (IT) capacity constraint encountered by electronic retailers in a duopolistic setting. There is a considerable amount of literature on advertising games between firms, yet introducing an IT capacity constraint fundamentally changes this problem. In the presence of information processing constraints, although advertising may still cause a customer to switch, it may not result in a sale, i.e., the customer may be lost by both firms. This situation could occur when customers have a limited tolerance for processing delays and leave the website of a firm because of slow response. In such situations, attracting more traffic to a firm's site (by increasing advertising expenditure) may not generate enough additional revenue to warrant this expenditure. We use a differential game formulation to obtain closedform solutions for the advertising effort over time in the presence of IT capacity constraints. Based on these solutions, we present several useful managerial insights.
This study compares the performances of pair development (an approach in which a pair of developers jointly work on the same piece of code), solo development, and mixed development under two separate objectives: effort minimization and time minimization. To this end, we develop analytical models to optimize module-developer assignments in each of these approaches. These models are shown to be strongly NP-hard and solved using a genetic algorithm. The solo and pair development approaches are compared for a variety of problem instances to highlight project characteristics that favor one of the two practices. We also propose a simple criterion that can reliably recommend the appropriate approach for a given problem instance. Typically, for efficient knowledge sharing between developers or for highly connected systems, the pair programming approach is preferable. Also, the pair approach is better at leveraging expertise by pairing experts with less skilled partners. Solo programming is usually desirable if the system is large or the effort needed either to form a pair or to code efficiently in pairs is high. Solo programming is also appropriate for projects with a tight deadline, whereas the reverse is true for projects with a lenient deadline. The mixed approach (i.e., an approach where both the solo and pair practices are used in the same project) is only indicated when the system consists of groups of modules that are sufficiently different from one another.
Media and network companies are increasingly providing digital media online. We develop a model to examine optimal strategies for media providers to utilize the online channel to distribute digital media. We examine a number of options for media providers. Our results suggest that media companies should sell programs online when content quality is relative high and online access cost is low. When online access cost is relative high, media providers could use the advertising strategy. Overall, companies are better off providing both pricing and advertising options to consumers. We derive the optimal price and advertising level, and analyze the factors that affect the price and advertising decisions. We find that as advertisement revenue rate increases, advertising level should be kept low. In addition, media companies should set online price and advertising level with consideration of the traditional channel in order to avoid channel cannibalization. We also analyze the advertising level in the traditional channel. Our results suggest that as digital video recorder technologies provide more convenience to consumers, media companies should increase, rather than decrease, revenues from advertising.