Kühne Logistics University

Control conditions are essential to establishing causal relationships in experimental management research, yet they receive little attention compared to treatments. This study thus examines the current state of control-condition selection and design in top-tier management journals, reviewing 958 experiments from 421 study papers published from 2021 to 2023. Our review shows that researchers use true and pseudo-control conditions. True control conditions—such as no-treatment, all-but-treatment, and treatment-as-usual controls—provide a baseline for interpreting the effect of the treatment condition. In contrast, pseudo-control conditions (e.g., opposite-treatment-level or alternative-treatment designs) allow relative comparisons across conditions without providing a baseline. Notably, 20% of the studies we examined presented causal claims that were not supported by their designs, opening the risk of their results being misinterpreted and their effect sizes being exaggerated. These issues were further exacerbated by a lack of method transparency and construct validity. In response, we offer guidelines not only for primary study researchers to support the selection and design of control conditions, thereby enhancing transparency and yielding valid interpretations of causal claims, but also for research synthesists, reviewers, and editors to evaluate the same.

With Artificial Intelligence (AI) entering the white-collar workforce at scale, employees will increasingly operate multiple AI agents which, once aligned, can (semi-) autonomously handle a wide array of tasks. Our commentary explores this prospective reality and its implications for employees. Specifically, we argue that employees in these scenarios will effectively have to become vertical multi-level managers, that is, continuously code-switching between the top, middle, and lower management roles when interacting with their AI agents. Against this background, we outline anticipated benefits and potential challenges for the involved employees. Finally, we discuss the implications of this transformative shift for future management research and the requirements we foresee for educational practices within our field.

Australia, a leading exporter of coal and gas, has long faced scrutiny for the perceived weakness of its climate policy. While accounting for only 1.5% of global greenhouse gas (GHG) emissions, it remains among the world's highest per-capita emitters. The Safeguard Mechanism, introduced in 2016 to prevent industrial emissions from offsetting national abatement efforts, was intended to play a central role in climate action. However, by 2020-21 emissions under the scheme had increased by 4.27%, and despite significant 2023 reforms, important deficiencies remain. This paper reviews the Safeguard Mechanism's evolution, structure and policy performance. Although the reforms introduced annually declining emissions baselines and strengthened compliance obligations, key weaknesses persist. Covered facilities may continue to rely extensively on offsets of uncertain integrity; baseline decline rates remain generous and flexible; emissions-intensive, trade-exposed industries retain preferential treatment; and carbon leakage risks are insufficiently addressed. A comparative analysis with the European Union Emissions Trading System (EU ETS) highlights alternative design features that have delivered measurable emissions reductions. In contrast to the Safeguard Mechanism, the EU ETS prohibits offset use, applies beyond only large emitters, has expanded sectoral coverage through the ETS2, and is reinforced by a carbon border adjustment mechanism to limit leakage. The paper concludes that further reform is required to broaden the Safeguard Mechanism's effectiveness and aligning Australia's industrial emissions trajectory with its net-zero commitments.

This paper addresses the planning of loading and unloading processes, also termed stevedoring, for Roll-on/Roll-off (RoRo) ships. We consider a setting where we sequence operations, assign tugs to handle trailers, and determine the cargo positions on the ship and in the yard. In real-world settings, the outcomes of these decisions are strongly affected by stochastic uncertainty, such as considering the type of cargo to unload or traffic congestion on the ship. To account for this, we formulate the problem as a sequential decision process and describe two feature-based policy function approximations for the decision-making. We propose an effective heuristic for designing policies in an offline learning process. We evaluate this approach in a case study at the Port of Kiel, Germany, simulating the service operations of a three-deck RoRo ship. Compared to industry benchmark policies, the proposed policies reduce the ship’s turnaround time by up to 26 min (8.8%). Additionally, we apply our methodology to illustrate the value of various levels of information availability. The results show that information on process completion times, especially for vehicle entry and exit at both the ship and the yard, has the potential to shave off turnaround time by an additional 8%. Yet, the availability of information on the incoming ship’s stowage plan is an important precondition for tailored policies capable of achieving such reductions, especially when there is a surplus of tug-handled cargo.