Technology-forcing regulations can compel incumbent firms to develop capabilities in technologies that lie outside their established knowledge bases. We examine how firms respond to this challenge by reconfiguring the geography and organization of invention. Focusing on the automotive industry's transition toward low-emissions technologies, we investigate whether more stringent environmental policies increase incumbent firms' reliance on technological hotspots—locations with specialized knowledge in low-emissions vehicle technologies. We examine two mechanisms through which firms can access this geographically concentrated knowledge: locating inventive activity in hotspots and collaborating with hotspot inventors. Using a comprehensive panel dataset covering more than half a million patent families from incumbent automotive manufacturers and their first-tier suppliers across 311 regions from 1990 to 2020, we find that technology-forcing regulation increases firms' propensity both to locate invention in low-emissions technology hotspots and to collaborate with inventors located there. These findings reveal an important spatial dimension of technology-forcing policy: by increasing firms' need to access specialized knowledge, regulation can reshape not only the direction of technological change but also where and with whom incumbent firms innovate.
Rail-transit timetables (e.g., light rail, suburban rail, and metro systems) have traditionally been established through hierarchical, top-down procedures where service frequencies are specified first, based on aggregated demand indicators or institutional targets, and detailed timetables are subsequently constructed to meet these specifications under operational constraints. Recent advances in data availability and modeling capabilities have enabled a shift toward what we term demand-driven timetabling. Unlike hierarchical approaches, demand-driven timetabling strategies directly optimize the timetable while accounting for time-varying passenger flows, thus jointly optimizing service quality and operational efficiency. We develop a detailed taxonomy of the main application scenarios, modeling archetypes and objective functions adopted in the demand-driven timetabling literature, and provide an analysis of the strategies used to represent train capacities and passenger boarding behavior. We also discuss the most prominent modeling features and assumptions, including rolling-stock circulation, dwell and travel time modeling, as well as energy-aware scheduling. Finally, we provide a comprehensive overview of the solution methodologies employed, ranging from exact and decomposition algorithms to metaheuristic and control-oriented approaches. We conclude by identifying key trends, limitations, and research gaps in the field, outlining future directions for adaptive timetable design to support the next generation of passenger-centric and energy-efficient rail-transit systems.
This study investigates the operational optimization of an on-demand, multi-modal relay-based delivery system comprising dispersed unmanned hub stations and fleets of drones and unmanned vehicles. We formulate a plan-based model to coordinate and synchronize heterogeneous delivery modes, ensuring timely and cost-effective fulfillment of customer orders. The proposed model determines which orders to accept in each arriving batch, allocates drones and vehicles to execute these orders, and schedules their movements to guarantee on-time deliveries. A nested exact algorithm, integrating logic-based Benders decomposition (LBBD), column-and-row generation (CRG), and branch-and-price (BP), efficiently solves typical instances in approximately 70 seconds. The algorithmic novelty lies in that a CRG & BP based exact algorithm is embedded in an LBBD to solve the LBBD’s subproblem. Computational experiments, based on realistic scenarios, demonstrate the effectiveness of our proposed methodology and provide several managerial insights. Factors such as average delivery distance, time-window flexibility, and fleet composition are shown to significantly impact system profitability and service quality. The results offer practical guidance on batch sizing, fleet configuration, and operational conditions under which multi-modal relay-based delivery systems outperform traditional courier-based models.
Artificial intelligence (AI) introduces a new paradigm that challenges established information systems (IS) frameworks, prompting a reassessment of IS use in AI-driven systems. To address this, we conducted a three-iteration multi-approach review of 52 articles centered on AI facets and system-level properties as drivers of AI use. Our review identifies and classifies AI’s facets (anthropomorphism, autonomy, inscrutability, and learning) and system-level properties (explainability, transparency, and reliability). We propose working definitions to resolve conceptual issues. Building on these findings, we inductively develop a framework that positions AI use as a process in which AI facets disrupt or enable user interactions, while system-level properties mediate these effects, individually or in combination. Together, these elements shape established and emerging forms of AI use and outcomes. Leveraging our framework and iterations insights, we critically assess extant understanding and propose a research agenda with actionable paths to support future IS research addressing AI use complexities.
Training-time privileged information (PI) can enable language models to succeed on tasks they would otherwise fail, making it a powerful tool for reinforcement learning in hard, long-horizon settings. However, transferring capabilities learned with PI to policies that must act without it at inference time remains a fundamental challenge. We study this problem in the context of distilling frontier models for multi-turn agentic environments, where closed-source systems typically hide their internal reasoning and expose only action trajectories. This breaks standard distillation pipelines, since successful behavior is observable but the reasoning process is not. We introduce π-Distill, a joint teacher–student framework that trains a PI-conditioned teacher and an unconditioned student simultaneously within a single shared-parameter model, enabling the teacher to learn how to use PI while mitigating distribution shift during transfer. We show that π-Distill effectively distills frontier agents using action-only privileged information, matching or outperforming industry-standard pipelines that assume access to full Chain-of-Thought supervision across multiple agentic benchmarks, models, and forms of PI. We complement our results with extensive analysis that characterize what factors enable effective learning with PI.
