This study examines learning effects (i.e., the effects of prior experience) in information systems development (ISD). ISD is characterized by disparate tasks, teams, and levels of project complexity across projects. These features challenge our understanding of how learning effects occur in the ISD context. Drawing on the theory of transfer of learning, this study examines how ISD project teams learn and under what conditions the learning effects are stronger or weaker. We find that ISD project teams’ experience in prior projects translates into performance gains for the current ISD project when the prior and current projects share the same domain, technology, or customer knowledge elements—domain, technology, and customer being the most essential knowledge types for ISD. Moreover, we find that the learning effects of domain, technology, and customer knowledge are substitutive for one another and that these learning effects become stronger or weaker depending on the extent of ISD projects’ team and task complexities. The study makes significant contributions to the ISD literature on learning effects and the roles of domain, technology, customer knowledge, and project complexity, as well as to the general organizational learning literature. It also provides important managerial insights into practical concerns such as project staffing and knowledge acquisition for ISD organizations. The online appendix is available at https://doi.org/10.1287/isre.2017.0713 .

We study a new dynamic order acceptance and assignment problem of a make-to-order manufacturing or service system that produces heterogeneous products or services using heterogeneous servers. Unlike traditional dynamic order acceptance and assignment problems, in our settings the system can strategically postpone acceptance and assignment decisions for orders in hand. The system does this while waiting for more profitable orders to come and/or more productive servers to become available, with the risk of losing orders in hand, low server utilization, and high waiting penalties. We formulate this problem as a stochastic dynamic program, characterize the structure of the optimal policy, and discuss managerial insights gained from the optimal policy. The paper also provides a methodological contribution by finding general structural properties and their preservation conditions, which can be reused in related future operations management studies.

For the successful and sustainable deployment and diffusion of smart factories, both the capabilities of the adopters who operate the factories and the capabilities of the suppliers who supply information technology and equipment play very important roles. However, since the existing models for diagnosing the capabilities of smart factories are mainly focused on evaluating the capabilities of the manufacturing companies themselves, such as technological capabilities and digital transformation, there are not many models that diagnose the supply capabilities of suppliers from the perspective of demand companies. Unlike models that diagnose the level of smart factories, when diagnosing the capabilities of suppliers, various factors such as supply experience and management capabilities must be comprehensively evaluated in addition to the capabilities of the company itself. Therefore, this study proposes a new model to diagnose the capabilities of suppliers from the perspective of adopters who want to build smart factories and verifies the validity of the model by applying the model for a pilot diagnosis for 32 suppliers. In addition, based on the survey results obtained from both adopters and suppliers participating in the pilot diagnoses, this study proposes an institutionalization plan for capability diagnosis.

Billions of dollars are exchanged between companies through accounts payable in today's business landscape. Given its immense scale and critical role in business operations, effectively managing accounts payable and working capital is essential for organizations. In this study, we examine the financial supply chain problem, where a company seeks to minimize total payments toward accounts payable received from its upstream partners (e.g., suppliers) while leveraging cash inflows from downstream partners (e.g., distributors, wholesalers, retailers, and customers). This is accomplished by optimizing payment decisions based on payment terms and capitalizing on interest gains from cash on hand over time. Unlike prior studies, we investigate this problem in a more realistic setting where information about incoming invoices and cash inflows is uncertain. We formulate the problem as a stochastic dynamic program and identify the structural properties of an optimal policy. The optimal policy reveals payment priorities among invoices and establishes thresholds for maintaining cash on hand. We further find that payment priorities can be deterministic or stochastic, depending on the problem's state and random parameters. Additionally, we identify all instances where payment priorities are deterministic. Our study provides valuable managerial insights and practical implications derived from the structural properties of the optimal policy. Notably, some of these insights challenge well-known heuristics and seemingly intuitive practices. Lastly, we develop a simple heuristic based on the identified structural properties and demonstrate that it outperforms other widely used methods for solving large-scale practical problems.

In the digital media landscape, YouTube has become crucial for promoting over-the-top (OTT) media content. This study investigates the effects of video marketer-generated content (VMGC) and video user-generated content (VUGC) on OTT viewership. Using Netflix's Top 10 movie viewership data and related YouTube content, we find that both VMGC and VUGC positively affect viewership. However, their interaction is negative, suggesting substitution. VUGC's positive effect is negatively moderated by video length and engagement, indicating a substitution effect, while VMGC's promotional effect is not. This difference might result from VMGC's careful design to promote without substituting the original content. Additional analysis reveals that spoilers do not drive VUGC's substitution effect. These findings highlight the importance of considering video content characteristics in understanding consumer behavior and demand for OTT content.