Institutt for regnskap, revisjon og foretaks­økonomi

European Journal of Operational Research Doi: https://doi.org/10.1016/j.ejor.2026.05.032

Conventional simulation models in healthcare are typically parameterised using historical data, limiting their ability to support responsive, real-time operational decision-making. They are therefore poorly suited to dynamic, high-pressure environments such as urgent and emergency care, where decisions must reflect the current system state. In this paper, we introduce real-time decision-support using simulation (RtS), a methodology that integrates real-time operational data into a discrete-event simulation model to enable context-aware, short-term decision-making. The model integrates partial real-time data feeds with historically derived distributions using a novel data fusion and model initialisation approach. By continuously aligning the simulation with the evolving system state, RtS enhances situation awareness and enables the evaluation of adaptive interventions whose implementation influences subsequent system behaviour. We implement the technical component of our RtS framework through a proof-of-concept model of an Urgent Care Network comprising multiple emergency and urgent care facilities. We evaluate resource-neutral operational policies designed to support short-term adaptation under conditions of rising demand. Our results demonstrate that, under the modelled high-demand conditions, normal emergency department operations can be restored within a 4-hour period through the implementation of two adaptive policies. By operationalising RtS in a service-based setting and demonstrating its effectiveness relative to conventional simulation, our findings provide healthcare stakeholders and OR practitioners with both the technical foundations and the scholarly framing needed to engage in mainstream discussions on deploying RtS as a step-change solution toward simulation enabled digital twins in complex sociotechnical systems.

Annals of Operations Research s. 1-34 Doi: https://doi.org/10.1007/s10479-026-07197-8

The stochastic flexible job-shop scheduling problem with random processing times is considered in this paper, where the makespan service level is maximized, i.e., the probability that a schedule is completed before a given deadline. In practice, this deadline corresponds to the scheduling horizon (e.g., a shift, a day or a week) during which the set of jobs to schedule must be completed. Compared to previous research, the uncertainty is related to the machine and the processing times of all operations processed on a machine are similarly impacted by the machine status. This helps to propose a new way of generating scenarios and deal with uncertainty. New bounds on the makespan service level are also proposed, and included in a tabu search approach. Computational experiments are conducted, that illustrate the relevance of the proposed approach. Several perspectives are proposed in the conclusions of the paper.

Pacific-Basin Finance Journal 98 s. 103163-103163 Doi: https://doi.org/10.1016/j.pacfin.2026.103163

This paper examines the impact of price limits, a unique feature of market microstructure in the Chinese stock market, on managerial learning from stock prices. We find that price limits hinder the efficiency of managerial learning by reducing the informativeness of stock prices. To address endogeneity concerns and establish causal inference, we exploit a quasi-natural experiment based on the 2020 reform of price limit rules on the ChiNext board, which relaxes daily price fluctuation constraints. We find that this reform significantly increases firms' investment sensitivity to stock prices, suggesting that managers can extract valuable information from market prices better when trading restrictions are loosened. Further analysis indicates that the negative effect of price limits is more pronounced in firms with management shareholding, low-ability management, and less over-confident management. We also find that the price limits hinder firms' investment efficiency. Overall, our findings shed light on the economic consequences of price limit regulation and provide empirical evidence to inform ongoing policy discussions.

Routledge Handbook of Auditing s. 209-226 Doi: https://doi.org/10.4324/9781032651545-18

The chapter highlights the differences between the audit of private and public companies, particularly considering the demand and supply for audits of private companies, and reviews the empirical literature on private company audits. The review is organized around the concept of audit quality and focuses on studies on the auditors’ ability to detect misstatements and auditor independence, as well as on audit quality inferred from proxy measures in private company audit settings. As access to credit can be of vital importance for private companies, we further review the literature investigating the effect of audits on the private clients’ access to credit, financing costs, and credit ratings. Finally, we review the literature on the auditor choice of private companies. To identify the relevant current literature, we conducted a systematic literature search. The review reveals that the majority of the current empirical literature on private company audits largely deals with issues that have already been addressed for public companies and does not exploit the unique opportunities that the private sector offers, such as lower market concentration, different incentives for auditors, and overall greater flexibility due to less regulation. We therefore call for more ingenuity in the choice of topics and research designs.

Magma forskning og viten 29(1) s. 147-158 Doi: https://doi.org/10.23865/magma.v29.1532

The emergence of agentic artificial intelligence (AI) agents—autonomous entities that pursue objectives, execute workflows, and interact across enterprise applications—marks the advent of a digital workforce operating alongside human employees. These systems promise efficiency and innovation, but challenge established assumptions about accountability, transparency, and control within organizations. In this article, we focus on internal corporate governance, understood as the structures, processes, and practices through which organizations direct, control, and assure operational activities. We examine how governance can adapt to the rise of agentic AI through the lens of the Three Lines Model of internal auditing. Our analysis addresses three questions: (1) How does the digital workforce transform internal governance challenges? (2) How must the roles of operational management, risk and compliance, and internal audit be reconceptualized? (3) What practical recommendations can be derived from applying the Three Lines Model to the governance of agentic AI? Our analysis indicates that effective governance of this future workforce requires sustained collaboration across the three lines, as the risks posed by agentic AI systems often span functional boundaries and cannot be managed in isolation. We propose strengthening collaboration in five domains: clarifying accountability, integrating AI agents into control environments, defining the scope of assurance, building cross-functional competencies, and aligning regulatory interpretations and responses. Further research is needed to understand how these mechanisms are institutionalized in practice and how internal governance systems must evolve in increasingly AI-enabled organizations.