Can data cooperatives sustain themselves?

Data cooperatives are emerging to empower consumers amidst a fast-changing data governance landscape. But they are not alone, and IT-enabled data marketplaces can be effective competitors. Sameer Mehta, Milind Dawande, and Vijay Mookerjee write that data cooperatives are not indispensable. They suggest four steps for data cooperatives to sustain themselves and thrive in this competitive market

Real Options and Software Upgrades: An Economic Analysis

This work extends earlier work on software upgrades as well as research on real options and IT investment. We consider a two-period model with one software provider who develops and releases a software product to the market. The result shows that the profit from the upgrade policy increases when the market size uncertainty increases. The option value of upgrade is higher when there is more market uncertainty. Also, the value of investing in design effort is more when the development cost is low

Modeling Coordination in Offshore Software Development

Controlling and minimizing coordination costs has been shown to be an important factor to reduce overall project performance in distributed software development. In this research-in-progress paper we investigate the effects of software complexity, software integration, distributed labor division policies, learning effects on software coordination costs. Drawing from data collected on 130 software construction cycles in 34 large projects of a leading offshore development firm, we first present our analysis on how coordination costs relate to team organization factors and complexity of evolving software. We base our analytic model of coordination costs in offshore software development on these empirical relationships, and give an overview of our modeling approach. We apply our model of software coordination costs to develop resource allocation policies in the projects we studied. We consider both waterfall and iterative software development methodologies and also tandem and parallel integration schemes. Our modeling approach helps managers to develop a dynamic coordination policy to aid iterative software development in distributed development environments

Designing Intelligent Expert Systems to Cope with Liars

To cope with the problem of input distortion by users of Web-based expert systems, we develop methods to distinguish liars from truth-tellers based on verifiable attributes, and redesign the expert systems to control the impact of input distortion. The four methods we propose are termed split tree, consolidated tree, value based split tree, and value based consolidated tree. They improve the performance of expert systems by improving accuracy or reduce misclassification cost. Numerical examples confirm that the most possible accurate recommendation is not always the most economical one. The recommendations based on minimizing misclassification costs are more moderate compared to that based on accuracy. In addition, the consolidated tree methods are more efficient than the split tree methods, since they do not always require the verification of attribute values

An Economic and Operational Analysis of the Market for Content Distribution Services

We develop an economic and operational model to examine the conditions for the viable provision of content distribution services by a monopolistic firm. Each user firm (the content provider or CP) has the option of buying content distribution services from the content distribution service provider (CDP) or going on its own to arrange for its content to be distributed at a set of chosen sites operated by Internet Service Providers (ISPs). The CDP enjoys operational benefits in terms of both the fixed and variable cost of replicating content. However, we find that for certain market situations (concentrated CP and ISP demand), not all CPs will find it attractive to buy services from the CDP. The best case for the CDP is when the various content providers have similar demand that is uniformly distributed across ISP sites

Do Recommender Systems Always Benefit Firms by Reducing Consumer Search Effort?

With the gain in popularity of the internet in the beginning of this decade, online shopping has witnessed a strong growth rate of about 25 percent per year. However, recent reports suggest that the growth rate is flattening. One of the key reasons for dwindling growth rate is the rise in expectations of the customers. Customers want that the retail websites should help them in finding products through recommendations. Therefore it is expected that firms would have higher profits by using recommender systems since that will boost their sales. However we show that increased profits are guaranteed only if a firm has the monopoly in the market. Market with two firms may not witness increased profits for both the firms. We analyze how the improvement in technology of recommender system affects the market, the price charged by the firms and therefore their profits

Information Security Investment with Different Information Types: A Two-Firm Analysis

We analyze information security investment decisions by two firms that possess imperfectly substitutable information assets. Information assets are imperfectly substitutable if information at each firm is valuable and becomes more valuable when combined. When compared to optimal investment decisions made by a central planner, we find diametrically opposite results in the case where these decisions are made independently: substitutable assets lead to an “arms race” in which both firms over-invest whereas complementary assets lead to under-provision of “public goods” in which both firms under-invest. We also find that firms with highly substitutable information assets may not necessarily increase the amount of security investment in a centralized investment environment as the intensity of the deflected cross traffic increases

How to Deal with Liars? Designing Intelligent Rule-Based Expert Systems to Increase Accuracy or Reduce Cost

Input distortion is a common problem faced by expert systems, particularly those deployed with a Web interface. In this study, we develop novel methods to distinguish liars from truth-tellers, and redesign rule-based expert systems to address such a problem. The four proposed methods are termed split tree (ST), consolidated tree (CT), value-based split tree (VST), and value-based consolidated tree (VCT), respectively. Among them, ST and CT aim to increase an expert system’s accuracy of recommendations, and VST and VCT attempt to reduce the misclassification cost resulting from incorrect recommendations. We observe that ST and VST are less efficient than CT and VCT in that ST and VST always require selected attribute values to be verified, whereas CT and VCT do not require value verification under certain input scenarios. We conduct experiments to compare the performances of the four proposed methods and two existing methods, i.e., the traditional true tree (TT) method that ignores input distortion and the knowledge modification (KM) method proposed in prior research. The results show that CT and ST consistently rank first and second, respectively, in maximizing the recommendation accuracy, and VCT and VST always lead to the lowest and second lowest misclassification cost. Therefore, CT and VCT should be the methods of choice in dealing with users’ lying behaviors. Furthermore, we find that KM is outperformed by not only the four proposed methods, but sometimes even by the TT method. This result further confirms the advantage necessity of differentiating liars from truth-tellers when both types of users exist in the population

Optimal Bidding for Mobile-Ad Campaigns

Self-service advertising platforms such as Cidewalk enable advertisers to directly launch their individual mobile advertising campaigns. These platforms contract with advertisers to provide a certain number of impressions on mobile apps in a specific geographic location (usually a town or a zip code) within a fixed time period (usually a day); this is referred to as a campaign. To meet the commitment for a campaign, the platform bids on an ad-exchange to win the required number of impressions from the desired area within the time period of the campaign. We address the platform’s problem of deciding its bidding policy to minimize the expected cost in fulfilling the campaign