Dengpan Liu

Efficient Techniques for Online Record Linkage

The need to consolidate the information contained in heterogeneous data sources has been widely documented in recent years. In order to accomplish this goal, an organization must resolve several types of heterogeneity problems, especially the entity heterogeneity problem that arises when the same real-world entity type is represented using different identifiers in different data sources. Statistical record linkage techniques could be used for resolving this problem. However, the use of such techniques for online record linkage could pose a tremendous communication bottleneck in a distributed environment (where entity heterogeneity problems are often encountered). In order to resolve this issue, we develop a matching tree, similar to a decision tree, and use it to propose techniques that reduce the communication overhead significantly, while providing matching decisions that are guaranteed to be the same as those obtained using the conventional linkage technique. These techniques have been implemented, and experiments with real-world and synthetic databases show significant reduction in communication overhead.

Consumer Learning and Time-locked Trials of Software Products

Manufacturers of information goods often offer free trial versions of their products. Information goods are experience goods, and trials often promote consumer learning with respect to quality. However, the downside of this strategy is that trials may cannibalize sales in the after-trial period. Recent research in information systems has identified this trade-off but has stopped short of comprehensively analyzing it. As a result, it has drawn unexpected and unrealistic conclusions, such as that offering a free time-locked trial of the fully functional version is optimal for “any” information good that does not exhibit significant network effects. We show that, when this trade-off is considered, a time-locked trial may not be optimal even in situations in which there is no network effect and the overall impact on consumers’ valuations is positive. With a simple model, we characterize the conditions necessary for optimality and explain their implications. The main insight is that, unless learning effects are appropriately incorporated in the analysis, there is a risk of overestimating the benefits of free trials. Using extensions to the basic model, we find that this insight is quite robust and applies to a wider context.

Advertising Strategies in Electronic Retailing: A Differential Games Approach

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 firms site (by increasing advertising expenditure) may not generate enough additional revenue to warrant this expenditure. We use a differential game formulation to obtain closed-form solutions for the advertising effort over time in the presence of IT capacity constraints. Based on these solutions, we present several useful managerial insights.

Resource Allocation Policies for Personalization in Content Delivery Sites

One of the distinctive features of sites on the Internet is their ability to gather enormous amounts of information about their visitors and to use this information to enhance a visitors experience by providing personalized information or recommendations. In providing personalized services, a website is typically faced with the following trade-off: When serving a visitors request, it can deliver an optimally personalized version of the content to the visitor, possibly with a long delay because of the computational effort needed, or it can deliver a suboptimal version of the content more quickly. This problem becomes more complex when several requests are waiting for information from a server. The website then needs to trade off the benefit from providing more personalized content to each user with the negative externalities associated with higher waiting costs for all other visitors that have requests pending. We examine several deterministic resource allocation policies in such personalization contexts. We identify an optimal policy for the above problem when requests to be scheduled are batched, and show that the policy can be very efficiently implemented in practice. We provide an experimental approach to determine optimal batch lengths, and demonstrate that it performs favorably when compared with viable queueing approaches.

Dynamic competition in IT security: A differential games approach

Hackers evaluate potential targets to identify poorly defended firms to attack, creating competition in IT security between firms that possess similar information assets. We utilize a differential game framework to analyze the continuous time IT security investment decisions of firms in such a target group. We derive the steady state equilibrium of the duopolistic differential game, show how implicit competition induces overspending in IT defense, and then demonstrate how such overinvestment can be combated by innovatively managing the otherwise misaligned incentives for coordination. We show that in order to achieve cooperation, the firm with the higher asset value must take the lead and provide appropriate incentives to elicit participation of the other firm. Our analysis indicates that IT security planning should not remain an internal, firm-level decision, but also incorporate the actions of those firms that hackers consider as alternative targets.

Optimal Software Reuse in Incremental Software Development: A Transfer Pricing Approach

This study develops optimal transfer pricing schemes that manage software reuse in incremental software development, namely, a development regime wherein users begin utilizing parts of the system that are released to them even before the system is entirely completed. In this setting, conflicts can arise between developers and users from divergent interests concerning the release of functionalities in the project. The release of functionalities is influenced by reuse, i.e., the effort spent by the development team to write code that can be reused within the same project or in future projects. For example, the development team may choose to spend extra effort to make certain portions of the system reusable because doing so could reduce the effort needed to develop the entire system. However, the additional effort spent on reuse could delay the release of certain critical functionality, making such a strategy suboptimal for the users. Thus, optimal reuse decisions for developers and users could be different. In addition, from the firms perspective, reuse decisions must not only balance the objectives of developers and users for the current project, but reuse effort may be spent to benefit future projects. Our study also highlights the fact that reuse may not always be beneficial for the firm. To this end, we consider different instances of the user--developer conflict and provide transfer pricing schemes that operate under information asymmetry and achieve two key properties: firm-level optimality and truth revelation. This paper was accepted by Sandra Slaughter, information systems.

Who's Next? Scheduling Personalization Services with Variable Service Times

Online personalization has become quite prevalent in recent years, with firms able to derive additional profits from such services. As the adoption of such services grows, firms implementing such practices face some operational challenges. One important challenge lies in the complexity associated with the personalization process and how to deploy available resources to handle such complexity. The complexity is exacerbated when a site faces a large volume of requests in a short amount of time, as is often the case for e-commerce and content delivery sites. In such situations, it is generally not possible for a site to provide perfectly personalized service to all requests. Instead, a firm can provide differentiated service to requests based on the amount of profiling information available about the visitor. We consider a scenario where the revenue function is concave, capturing the diminishing returns from personalization effort. Using a batching approach, we determine the optimal scheduling policy (i.e., time allocation and sequence of service) for a batch that accounts for the externality cost incurred when a request is provided service before other waiting requests. The batching approach leads to sunk costs incurred when visitors wait for the next batch to begin. An optimal admission control policy is developed to prescreen new request arrivals. We show how the policy can be implemented efficiently when the revenue function is complex and there are a large number of requests that can be served in a batch. Numerical experiments show that the proposed approach leads to substantial improvements over a linear approximation of the concave revenue function. Interestingly, we find that the improvements in firm profits are not only (or primarily) due to the different service times that are obtained when using the nonlinear personalization function—there is a ripple effect on the admission control policy that incorporates these optimized service times, which contributes even more to the additional profits than the service time optimization by itself.

Quality, Pricing, and Release Time: Optimal Market Entry Strategy for New Software-as-a-Service Vendors

As a new software licensing model, software-as-a-service (SaaS) is gaining tremendous popularity across the globe. In this study, we investigate the competition between a new entrant and an incumbent in a SaaS market, and derive the optimal market entry strategy for the new entrant. One interesting finding is that, when the new entrant’s product is fully compatible with that of the incumbent, but has a significantly lower quality, the new entrant should adopt an instant-release strategy, i.e., releasing its product at the start of the planning horizon. If the initial quality gap of the two products is small, the new entrant is better off adopting a late-release strategy, i.e., deferring the release of the new product until its quality surpasses that of the existing product. When the two competing products are partially compatible, in addition to instant-release and late-release, an early-release strategy, i.e., spending some time improving the quality of the new product but releasing it before its quality reaches that of the existing product, can also be optimal. Furthermore, under full product compatibility, higher quality leads to higher price for the new product, whereas under partial product compatibility, higher quality does not always go in tandem with higher price.