Knowledge that Transforms

In this paper we address the problem of increasing software maintenance costs in a custom software development environment, and develop a stochastic decision model for the maintenance of information systems. Based on this modeling framework, we derive an optimal decision rule for software systems maintenance, and present sensitivity analysis of the optimal policy. We illustrate an application of this model to a large telecommunications switching software system, and present sensitivity analysis of the optimal state for major upgrade derived from our model. Our modeling framework also allows for computing the expected time to perform major upgrade to software systems.

This paper presents a project management policy in which the appearance of software faults during system construction is used to determine the timing of system integration activities (e.g., team meetings, analyzing modules for interface inconsistencies, system fault correction, and so on). System integration is performed only if a threshold fault count has been exceeded; otherwise, module development is allowed to continue. We derive an expression for calculating fault thresholds and analyze the policy to reveal the presence of three operating regions: (1) a region in which development should continue with no system integration, (2) a region in which system integration occurs if a threshold fault count has been exceeded, and (3) a region in which system integration should always take place. Analytical and numerical results demonstrate how the fault thresholds change with system complexity, team skill, development environment, and project schedule. We also show how learning that occurs during each round of system integration leads to less frequent integration in the future, and lower total construction effort. Simulation experiments reveal that the fault threshold policy can be applied even if several homogeneity assumptions in the model are relaxed, allowing for differences in the propensity among modules to accumulate faults and the effort needed to correct these faults. Finally, the fault threshold policy outperforms a fixed-release policy in which system integration occurs whenever a fixed number of modules has been released.

This paper presents an approach to organizational modeling that combines both agent-centric and activity-centric approaches. Activity-centric approaches to process modeling capture the mechanistic components of a process (including aspects of workflow, decision, and information), but agent-centric approaches capture specific aspects of the human component. In this paper, we explore an integrative viewpoint in which the transactional aspects of agent-centric concerns—for example, economic incentives for agents to perform—are integrated with decision and informational aspects of a process. To illustrate issues in this approach, we focus on modeling incentive mechanisms in a specific sales process and present results from an extensive simulation experiment. Our results highlight the importance of considering the effects of incentives when decision and informational aspects of a process undergo changes.

We use an economic model to formalize the complex relationships among IT investments, intermediate performance measures (e.g., product quality and output levels), and economic performance (e.g., productivity, profits, and consumer surplus). We demonstrate that a profit-maximizing monopolist invests in IT (modeled as changes in parametric characteristics of the firm) to design a better-quality product and charge a higher price. While this profit-maximizing adjustment generates more consumer surplus, it also increases production costs in a way that adversely affects productivity. In contrast, a simple model extension shows that when a firm is unwilling or unable to improve product quality, then IT investments result in suboptimal improvements in profits, an increase in consumer surplus, and an increase in productivity. Together, these models highlight the way in which product quality moderates the relationship between IT investments and economic performance. We also demonstrate that these relationships are robust to the socially optimal case in which a social planner chooses price and quality to maximize social welfare. In addition, we demonstrate that the results of the monopoly model hold when considering the design and development of products offered free of charge (e.g., free online content), but that provide indirect benefits to the firm (e.g., more advertising revenues).

We study the process by which model-based decision support systems (DSSs) influence managerial decision making in the context of marketing budgeting and resource allocation. We focus on identifying whether and how DSSs influence the decision process (e.g., cognitive effort deployed, discussion quality, and decision alternatives considered) and, as a result, how these DSSs influence decision outcomes (e.g., profit and satisfaction both with the decision process and the outcome). We study two specific marketing resource allocation decisions in a laboratory context: sales effort allocation and customer targeting. We find that decision makers who use high-quality, model-based DSSs make objectively better decisions than do decision makers who only have access to a generic decision tool (Microsoft Excel). However, their subjective evaluations (perceptions) of both their decisions and the processes that lead to those decisions do not necessarily improve as a result of DSS use. And expert judges, serving as surrogates for top management, have a difficult time assessing the objective quality of those decisions. Our results suggest that what managers get from a high-quality DSS may be substantially better than what they see. To increase the inclination for managerial adoption and use of DSS, we must get users to “see” the benefits of using a DSS. Our results also suggest two ways to bridge the perception-reality gap: (1) improve the perceived value of the decision process by designing DSSs both to encourage discussion (e.g., by providing explanation and support for alternative recommendations) as well as to reduce the perceived complexity of the problem so that managers invest more cognitive effort in exploring additional options and (2) provide feedback on the likely market/business outcomes of various decision options.

The widespread use of the Internet has led to the emergence of numerous information intermediaries that bring buyers and sellers together and leverage their knowledge of the marketplace to provide value-added services. Infomediaries offer matching services that facilitate establishment of a buyer-seller agreement, and value-added services that either provide a standalone benefit or enhance benefits from matching services. This paper develops and analyzes economic models of intermediaries to examine their pricing and product line design strategies. Intermediaries provide aggregation benefits: Buyers find an intermediary’s service more valuable if it provides access to more sellers, and sellers value it more if it provides access to more buyers, but also when they compete with fewer sellers. Due to this unique combination of network effects, we find that an intermediary has stronger incentives to provide quality-differentiated versions of its service relative to other information goods sellers. When buyers have constant marginal valuations for service quality, the intermediary should offer only two levels of service. While it is optimal for the intermediary to offer two levels of service, increasing the quality of the low-level service reduces the intermediary’s profits due to increased cannibalization of the premium service. Hence, the optimal menu consists of a basic matching service and a premium service that includes matching and value-added services. The intermediary’s profits are larger when positive network effects are stronger, and lower when negative network effects are stronger.