Code reuse is a form of knowledge reuse in software development that is fundamental to innovation in many fields. However, to date there has been no systematic investigation of code reuse in open source software projects. This study uses quantitative and qualitative data gathered from a sample of six open source software projects to explore two sets of research questions derived from the literature on software reuse in firms and open source software development. We find that code reuse is extensive across the sample and that open source software developers, much like developers in firms, apply tools that lower their search costs for knowledge and code, assess the quality of software components, and have incentives to reuse code. Open source software developers reuse code because they want to integrate functionality quickly, because they want to write preferred code, because they operate under limited resources in terms of time and skills, and because they can mitigate development costs through code reuse.
The measurement of customer lifetime value is important because it is used as a metric in evaluating decisions in the context of customer relationship management. For a firm, it is important to form some expectations as to the lifetime value of each customer at the time a customer starts doing business with the firm, and at each purchase by the customer. In this paper, we use a hierarchical Bayes approach to estimate the lifetime value of each customer at each purchase occasion by jointly modeling the purchase timing, purchase amount, and risk of defection from the firm for each customer. The data come from a membership-based direct marketing company where the times of each customer joining the membership and terminating it are known once these events happen. In addition, there is an uncertain relationship between customer lifetime and purchase behavior. Therefore, longer customer lifetime does not necessarily imply higher customer lifetime value. We compare the performance of our model with other models on a separate validation data set. The models compared are the extended NBD–Pareto model, the recency, frequency, and monetary value model, two models nested in our proposed model, and a heuristic model that takes the average customer lifetime, the average interpurchase time, and the average dollar purchase amount observed in our estimation sample and uses them to predict the present value of future customer revenues at each purchase occasion in our hold-out sample. The results show that our model performs better than all the other models compared both at predicting customer lifetime value and in targeting valuable customers. The results also show that longer interpurchase times are associated with larger purchase amounts and a greater risk of leaving the firm. Both male and female customers seem to have similar interpurchase time intervals and risk of leaving; however, female customers spend less compared with male customers.
Past research in new product development (NPD) has conceptualized prototyping as a “design-build-test-analyze” cycle to emphasize the importance of the analysis of test results in guiding the decisions made during the experimentation process. New product designs often involve complex architectures and incorporate numerous components, and this makes the ex ante assessment of their performance difficult. Still, design teams often learn from test outcomes during iterative test cycles enabling them to infer valuable information about the performances of (as yet) untested designs. We conceptualize the extent of useful learning from analysis of a test outcome as depending on two key structural characteristics of the design space, namely whether the set of designs are “close” to each other (i.e., the designs are similar on an attribute level) and whether the design attributes exhibit nontrivial interactions (i.e., the performance function is complex). This study explicitly considers the design space structure and the resulting correlations among design performances, and examines their implications for learning. We derive the optimal dynamic testing policy, and we analyze its qualitative properties. Our results suggest optimal continuation only when the previous test outcomes lie between two thresholds. Outcomes below the lower threshold indicate an overall low performing design space and, consequently, continued testing is suboptimal. Test outcomes above the upper threshold, on the other hand, merit termination because they signal to the design team that the likelihood of obtaining a design with a still higher performance (given the experimentation cost) is low. We find that accounting for the design space structure splits the experimentation process into two phases: the initial exploration phase, in which the design team focuses on obtaining information about the design space, and the subsequent exploitation phase in which the design team, given their understanding of the design space, focuses on obtaining a “good enough” configuration. Our analysis also provides useful contingency-based guidelines for managerial action as information gets revealed through the testing cycle. Finally, we extend the optimal policy to account for design spaces that contain distinct design subclasses.
This paper provides insights about when versioning is an optimal strategy for information goods. Our characterization of this class of goods is that variable costs are invariant with quality, including the special case of zero variable costs. Our analysis assumes a monopoly firm that has an existing product in the market and has an opportunity to segment the market by introducing additional lower-quality versions. We derive a simple decision rule for determining the optimality of versioning based on the solution to a single-product maximization problem. Versioning is optimal when the optimal market share of the lower-quality version, offered alone, is greater than the optimal market share of the high-quality version, offered alone. A firm can profitably employ versioning for an information good if it can design the lower quality in a way that, relative to their valuations for the high-end version, high-type consumers have a lower relative valuation for the lower quality than do low-type consumers. When variable costs increase, a firm that offered only one product version need not consider adding another version. When variable costs decrease, the firm should explore adding a lower-quality version.
We take advantage of recent advances in optimization methods and computer hardware to identify globally optimal solutions of product line design problems that are too large for complete enumeration. We then use this guarantee of global optimality to benchmark the performance of more practical heuristic methods. We use two sources of data: (1) a conjoint study previously conducted for a real product line design problem, and (2) simulated problems of various sizes. For both data sources, several of the heuristic methods consistently find optimal or near-optimal solutions, including simulated annealing, divide-and-conquer, product-swapping, and genetic algorithms.
Manufacturers have recently begun outsourcing product assembly and completion tasks to their suppliers. Such outsourcing solves several contracting problems but generates new incentive frictions between manufacturers and their suppliers. In this paper, we analyze a manufacturer's decision to outsource an assembly (second-stage) task to a preestablished supplier. We find that outsourcing second-stage tasks becomes more attractive as the cost of either the first- or second-stage activity rises. Outsourcing becomes less attractive when the supplier is unable to accept large levels of liability. The manufacturer is shown to prefer more testing when she outsources assembly to her supplier as opposed to when she assembles products in house. Last, we find that the contracting frictions identified persist when the supplier's work can be tested individually, albeit imperfectly.
The newsvendor model captures the trade-off faced by a decision maker that needs to place a firm bet prior to the occurrence of a random event. Previous research in operations management has mostly focused on deriving the decision that minimizes the expected mismatch costs. In contrast, we present two methods that estimate the unobservable cost parameters characterizing the mismatch cost function. We present a structural estimation framework that accounts for heterogeneity in the uncertainty faced by the newsvendor as well as in the cost parameters. We develop statistical methods that give consistent estimates of the model primitives, and derive their asymptotic distribution, which is useful to do hypothesis testing. We apply our econometric model to a hospital that balances the costs of reserving too much versus too little operating room capacity to cardiac surgery cases. Our results reveal that the hospital places more emphasis on the tangible costs of having idle capacity than on the costs of schedule overrun and long working hours for the staff. We also extend our structural models to incorporate external information on forecasting biases and mismatch costs reported by the medical literature. Our analysis suggests that overconfidence and incentive conflicts are important drivers of the frequency of schedule overruns observed in our sample.
In this paper, we study a firm that faces demand from two sources: demand for new items and demand to replace failed items under warranty. We model this setting as a multiperiod single-product inventory problem where the demands for new items in different periods are independent and the demands for replacing failed items depend on the number and ages of the items under warranty. We consider backlogging and emergency supply cases, and study both discounted cost and average cost criteria. We prove the optimality of the w-dependent base-stock ordering policy where the base-stock level is a function of w, the vector representing the number of items at different ages currently under warranty. For the special case where the demand for new products is identically distributed, we prove the optimality of a stationary w-dependent base stock policy for the finite-horizon discounted and the infinite-horizon discounted and average cost cases. In our computational study, we find that an optimal w-dependent policy can lead to 69% average improvement in expected costs when compared to a policy that neglects demands from items under warranty.
Motivated by the medical literature findings that physicians are inertial, we seek to understand (1) whether physicians exhibit structural persistence in drug choice (structural persistence occurs when the drug chosen for a patient depends structurally on the drug previously prescribed by the physician to other patients) and (2) whether persistence, if present, is a physician-specific characteristic or a physician state that can change over time. We further explore the role of promotional tools on persistence and drug choice, and we investigate whether physicians who exhibit persistence respond differently to three forms of sales promotion: one-to-one meetings (detailing), out-of-office meetings, and symposium meetings. Our results show significant levels of physician persistence in drug choice. We find that persistence is mostly a cross-sectional physician feature. Nonpersistent physicians appear to be responsive to detailing and symposium meetings, whereas persistent physicians seem to be responsive only to symposium meetings. Out-of-office meetings, such as golf or lunch, have no effect on physicians' drug choice. We also find that (1) older physicians and those who work in smaller practices are more likely to be persistent and (2) physicians who are more willing to receive sales force representatives have a lower likelihood of being persistent. Finally, we discuss implications for public policy from our rich set of results.
This paper provides empirical evidence on the effect of product introduction delays on accounting-based measures of operating performance. Based on a diverse set of 450 publicly traded firms that experienced product introduction delays, we find that delays have a statistically significant negative effect on profitability. Depending on the method used to estimate abnormal performance, the median abnormal decline in return on assets (ROA) ranges from 2.70% to 3.44% over a three-year period around the year of the delay announcement. The median decline in sales over assets ranges from 5.92% to 10.99%, and the median decline in return on sales ranges from 1.48% to 3.06%. Cross-sectional regression analysis indicates that the impact of delays on abnormal ROA is more negative for smaller firms, and for firms that are more profitable before the delay. Furthermore, the impact is more negative for firms that operate in industries that are larger and more profitable. We also find a positive association between abnormal ROA and abnormal stock price performance around the product introduction delay announcements.