Kühne Logistics University

Context Urban dairy production systems in developing countries play a crucial role in enhancing food security. However, they operate under significant constraints of land scarcity and market inefficiencies. This has led to a transition towards dairy-intensive production systems. These systems mainly focus on High Input Production (HIP) and Market Digital Integration (MDI) strategies. However, the empirical evidence of the impact of these strategies on a holistic sustainability index remains poorly quantified and limited. Objective The aim was to assess the frequency of practicing these strategies and quantify the causal impact of practicing HIP and MDI intensification strategies individually or in combination on a holistic Sustainability Index (SI). Methods The study used cross-sectional data from 304 urban dairy farmers in Nairobi, Kenya. Using Factor Analysis (FA), data on sustainability indicators were normalized, weighted, and aggregated to generate a composite sustainability index as well as the dimensional sub-indices. The Multivariate Endogenous Treatment Effects Model (METEM) was employed to estimate the impact of intensification on a calculated sustainability index. Results and conclusions Results reveal urban dairy farmers are highly educated and experienced, operating on an average of 1.7 acres of land. The frequency of practicing these strategies were uneven, with HIP being the most common (71%), followed by the combined strategy (10%), and MDI being the least common (6%). Output from METEM shows that the HIP, combined strategy, and MDI strategy significantly enhanced the sustainability index by 51%, 54%, and 8%, respectively. The modest magnitude of the complementarity gains from the combined strategy compared to HIP suggests that realizing the full complementary potential of combined adoption remains constrained by managerial complexities for farmers operating under limited extension support. Significance Evidence from this study strongly supports the need for targeted support programs that not only promote the practice of these intensification strategies but also provide the crucial targeted extension and advisory support required for optimized combined use, leading to more productive and sustainable urban dairy systems.

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.

Purpose This study aims to address important gaps in understanding public primary healthcare (PPHC) supply chain resilience in Sub-Saharan African (SSA) countries, with a focus on the complex interactions between routine healthcare delivery and emergency response during infectious disease outbreaks (IDOs). Design/methodology/approach The authors developed a system dynamics model, the health supply chain resilience (HSCR) model, to integrate epidemiological dynamics, supply chain operations and healthcare service delivery in SSA contexts. The model simulates the effects of IDOs on the interaction between routine and emergency healthcare delivery and evaluates supply chain preparedness strategies. Three types of interventions were assessed quantitatively: prepositioning of health commodities, local sourcing strategies and healthcare workforce expansion. Findings The analysis highlights the inherent trade-offs between emergency response and routine healthcare continuity. Combining complementary preparedness measures results in stronger resilience outcomes than individual interventions. The HSCR model supports the quantification of these trade-offs and the evidence-based prioritization of investments under resource constraints. Practical implications The model informs policymakers and healthcare professionals on how to design and prioritize supply chain preparedness strategies that balance emergency response and routine healthcare delivery. Originality/value This research contributes to the existing healthcare resilience literature as a novel tool to operationalize resilience, supporting evidence-based decision-making for health system stakeholders. It bridges theoretical and practical approaches to resilience-building in SSA national healthcare systems, offering a replicable model adaptable to diverse disease profiles and country settings, such as low- and middle-income countries with centralized PPHC systems.

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.