All KLU Publications

Extreme weather is often perceived as a major threat to the reliability of rail transportation. This study investigates regional rail operations in central Germany and examines whether severe weather conditions causally affect train arrival delays. We combine several years of operational data (2017–2022) with weather observations from the German Weather Service and define four component-specific treatments for adverse conditions: extreme temperature, strong wind, heavy rainfall, and snow presence. To estimate causal effects, we employ a Double Machine Learning framework based on partial linear regression (PLR–DML) and Causal Forest–DML. By reframing the weather–delay relationship as a causal question rather than a purely predictive one, the study provides evidence on whether extreme weather constitutes a materially relevant source of arrival delays. Across all four weather components, estimated average treatment effects are small in magnitude; analyses of individualized and group-level effects reveal no consistent or robust patterns of heterogeneity across lines, seasons, weekdays, or hours of the day. Robustness checks indicate that these operationally negligible estimates are not sensitive to outliers, rare-event imbalance, or to reasonable perturbations consistent with plausible unobserved confounding. Exploratory mediation analyses are consistent with the interpretation that passenger-flow variables do not materially amplify the already negligible estimated effects of severe weather on delays. Overall, the results suggest that, in this regional network, severe weather does not materially increase arrival delays. The findings underscore the importance of rigorous causal diagnostics for distinguishing perceived from materially relevant sources of delay in transportation reliability studies.

Since the first publications on humanitarian operations, researchers have been concerned about the impact of our research on humanitarian practice. This paper seeks to better understand the gap between research and practice and, thus, how to narrow it. We incorporate not only the research perspective, through a review of 640 abstracts in humanitarian operations and a survey of the author community, but also the practitioner perspective, through 18 interviews with experienced humanitarian practitioners and a review of 131 practitioner conference proceedings. We assess the extent of practice impact, and find limited but important instances. We identify new barriers to achieving practice impact from the practitioner perspective. Finally, we develop a framework for describing the depth and reach of practice impact achieved by a research project, with a description of many ways in which such impact may be (and has been) achieved. We hope these efforts enable greater researcher accountability to practitioners and clearer paths to practice impact for the field.

Effective resource allocation is crucial for optimizing business processes. Yet, most existing methods focus solely on single-process optimization, overlooking the interdependencies present in multi-process environments. This limitation results in inefficient resource allocation, and scalability challenges. To address this gap, we propose MuProMAC (Multi-Process Multi-Agent Coordination), a novel reinforcement learning-based method designed to optimize resource allocation across multiple interdependent business processes. Unlike prior methods, MuProMAC is the first online resource allocation method that explicitly models the interdependencies between processes and dynamically balances competing resource demands to minimize global average cycle time. We evaluate our method in five multi-process scenarios with different levels of resource contention, comparing it against state-of-the-art online resource allocation methods and three simple baselines. Our results show that MuProMAC is consistently among the top-performing methods in shared-resource environments. It achieves low cycle times and stable performance across different workload conditions, outperforming existing methods through its strong adaptability to evolving business processes and increasing complexity.

Experiments are a powerful method for establishing causal inference and have increasingly been used in international business (IB). Yet the method remains in its infancy, standards remain unclear, and IB is particularly challenging for experimental designs despite its promise. IB experiments are also scattered across a wide range of (non-IB) journals. We therefore conduct a comprehensive review of experiments in IB to assess how, where, and with what rigor experimental methods are currently being used, and where methodological weaknesses may limit their contributions to IB and adjacent fields. We identify and analyze 212 experiments since 2000 across IB and management journals. Our review highlights both the opportunities and methodological challenges of experimentation in IB, with particular attention to issues of external validity, internal validity, and transparency. We provide an IB-specific design guide to address these challenges, illustrate how experiments can strengthen theory building in cross-border contexts, and outline opportunities for impactful future research at the intersection of IB and experimental methods.

Biomethane production has expanded rapidly in France, making it the leading producer in Europe in 2024, largely supported by feed-in tariffs. Recent institutional reports nevertheless argue that these policies generate excessive profits, particularly for agricultural producers. This paper revisits these claims by examining how inter-organizational arrangements shape profitability measurement in the biomethane sector. Drawing on the literature on transfer pricing and business models, we argue that profitability measured at the legal-entity level may diverge from underlying economic performance when production is organized across interconnected entities. Combining a quantitative analysis of ownership structures for 273 biomethane units with 23 in-depth case studies, we identify two main sources of distortion. First, arrangements affecting fixed assets and biomass inputs may disconnect recorded costs from market conditions. Second, productive activities such as biomass handling, transport, and digestate spreading may be performed outside the biomethane entity itself, shifting labor and operating costs across organizations. We further show that these arrangements are strongly shaped by ownership structures, giving rise to three distinct business models of biomethane production that bias profitability indicators in different ways. These findings question the robustness of firm-level profitability measures used to recalibrate feed-in tariffs and, more broadly, highlight the need to integrate organizational structures into renewable energy policy evaluation.

Process mining has grown into a mature research field with a wide range of techniques and applications. Much of this development builds on the pioneering work of Wil van der Aalst, whose contributions have shaped both the foundations and the growth of the discipline. Today, the field’s breadth raises the need for systematic methodological reflection to ensure that findings are robust and meaningful. In this paper, we provide a comprehensive discussion of threats to validity in process mining research. Building on the methodological framework of algorithm engineering, we analyze nine distinct validity concerns and examine how they apply across different streams of process mining. Our analysis highlights both established strengths and recurring challenges, drawing on examples from seminal contributions in the field, many inspired by Wil’s work.

The Colombian road freight transport sector is a major contributor to greenhouse gas (GHG) emissions and plays a critical role in achieving the country’s net-zero target by 2050. While technological solutions such as vehicle electrification and alternative fuels are essential, substantial mitigation potential also lies in improving operational efficiency. A persistent source of inefficiency is the prevalence of empty trips, cargo-free freight movements that increase logistics costs, reduce fleet utilisation, and generate avoidable emissions.
This study addresses empty trips from a national, system-level planning perspective by introducing a spatio-temporal synchronisation model that identifies opportunities to coordinate independent freight movements travelling in opposite directions based on temporal and spatial compatibility. By relying on aggregated information rather than firm-level routing optimisation, the model enables scalable coordination across the freight network without requiring centralised control.
Two algorithms were developed to estimate current empty-trip rates from observed trip sequences and the minimum achievable rate under ideal synchronisation. Applied to more than 7.99 million trips from Colombia’s National Freight Registry, the results indicate that synchronisation could reduce empty kilometres by up to 39.34% for light and medium-duty vehicles and 37.37% for heavy-duty vehicles, corresponding to an estimated annual reduction of 130,470 tons of CO2e.
The study also introduces the concept of structural residual empty trip revenue (SRETR) to assess the economic potential of empty trips that remain unavoidable under optimal coordination. These insights support policymakers in prioritising interventions, addressing regional freight imbalances, and integrating operational efficiency measures into national decarbonisation strategies.

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.

Despite growing regulatory concerns about potential overcharging of sustainable investors, empirical evidence is lacking. In two controlled laboratory-in-the-field experiments with 415 professional financial advisors from Europe and the United States and an incentivized survey, we identify two distinct but interacting effects. First, advisors charge sustainable investors a premium. This premium persists even when accounting for differences in skill, effort, and costs. Second, advisors impose higher fees on clients with low financial literacy. These factors interact. Sustainable investors with low financial literacy are charged the highest fee, whereas those with high financial literacy do not pay a sustainability premium. Our findings suggest that advisors extract additional fees for sustainable investment mandates but avoid overcharging sustainable investors with high financial literacy.

Process mining aims to obtain insights from event logs through the automated analyses of recorded process data in information systems, with the ultimate aim to improve business processes running in organisations. However, real-life event logs are often incomplete, noisy, or ambiguous, such as missing timestamps or having ambiguous event labels, which traditional deterministic models cannot capture. Recent process mining developments have considered uncertainty in process mining artifacts more explicitly: in logs of recorded process behaviour, uncertainty may implicitly or explicitly influence process mining outcomes, while in process models, explicit uncertainty allows analysts to interpret and value outcomes. In this paper, we provide a conceptual foundation for uncertainty in process mining by introducing a four-level specification that separately addresses uncertainty in log attributes (e.g., activity labels of events, frequencies) and model elements (e.g., service times, read guards). For each type of uncertainty, we illustrate the levels with concrete examples to help understanding and application. We then provide a structured overview of the state of the art in stochastic process mining, classified using our specification, and present key open research challenges.

When binary classification models are wrong, managers face misclassification costs. Although false positive outcomes imply unnecessary mitigation efforts, false negative outcomes imply overlooking the class of interest. Humans calibrate these ai models supporting operational systems by adjusting the decision threshold that translates prediction probability into either class. Results of our controlled laboratory experiment show that, despite all relevant information being available, decision makers systematically deviate from the optimal cost-efficient threshold. We observe a significant interaction effect of class and cost imbalance on this deviation, which increases in high-stakes settings where more extreme thresholds are optimal. When unit costs are different, we find that participants anchor on the threshold where expected misclassification costs for false alarms and missed hits are equal, whereas mean anchoring cannot explain the pull-to-center behavior sufficiently. Surprisingly, we confirm that this impulse balance equilibrium also serves as attractive anchor in our setting, where decisions are made ex ante without loss aversion. To debias decision makers, simulated responses with behavior-aware costs show that subjects are nudged to make choices closer to the optimum. Managers should be aware of this boundedly rational behavior and complementary debiasing techniques, as sub-optimal threshold setting results in 53% higher misclassification costs, on average.

An estimated 305 million people required humanitarianassistance in 2025, even as official donor fundingcontinued to contract. Under these constraints,logistics — historically treated as an operational lineitem — is a determinant of whether responses happenat all, and transportation choices about modalities,providers, and financial arrangements shape deliverytimelines, geographic reach, and operational risk. Yetevidence on when donated transport is used, howdecisions are made on both sides of the partnership,and what effects these arrangements have onresponse capacity remains limited. This report providesempirical evidence on when these partnerships areactivated, how they create value, and where they canbe strengthened.

High-performing employees (HPEs) contribute disproportionate value to organizations not only through exceptional task performance but also through discretionary helping behaviors toward coworkers. Yet prior research offers mixed evidence regarding HPEs’ engagement in organizational citizenship behavior directed at individuals (OCB-I), portraying them alternately as highly productive “all-stars” or isolated “lone wolves.” We argue that this inconsistency reflects differences in how HPEs interpret coworkers’ reactions to their success, particularly envy, and how these interpretations are shaped by relational context. Drawing on the envy-attribution model and Emotions as Social Information (EASI) theory, we develop a moderated mediation framework in which HPEs’ perceptions of coworkers’ envy, whether benign or malicious, explain when high-performance status translates into OCB-I, and how relational identification shapes these interpretations. Across two vignette experiments (N = 837), a two-wave field study (N = 233), and a laboratory experiment (N = 197), we find convergent evidence that perceiving benign envy increases OCB-I, whereas perceiving malicious envy decreases it. Relational identification selectively reduces perceptions of malicious envy and buffers its negative indirect effect on OCB-I but does not significantly shape perceptions of benign envy or its positive behavioral consequences.

This article reports on the 6th meeting of the working group AG MARKETING within the GfKl Data Science Society. The meeting was held on May 10, and 11, 2024 at Kühne Logistics University in Hamburg, Germany. The meeting constituted the kick-off meeting for a special issue in Schmalenbach Journal of Business Research under the theme „The Value of User Generated Data for Managerial Decision Making“ with Raoul V. Kübler, Alexa B. Burmester, Friederike Paetz and Martin Klarmann as Guest Editors.

Operations and Supply Chain Management (OSCM) has continually evolved, incorporating a broad array of strategies, frameworks, and technologies to address complex challenges across industries. This encyclopedic article provides a comprehensive overview of contemporary strategies, tools, methods, principles, and best practices that define the field’s cutting-edge advancements. It also explores the diverse environments where OSCM principles have been effectively implemented. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners.

Checklists are among the most widely used instruments for guiding work, yet they have received little attention in Business Process Management (BPM) research. In 2017, we published a paper that proposed to view checklists as informational artifacts and called for a “science of checklists”. The paper proposed a conceptualization of checklists in terms of seven properties, analyzed twenty-one recurring problems with their use, and argued that a design-oriented, informational approach could be used to overcome many of these problems. The paper was published and has since been cited about thirty times. However, the research agenda it set out was not taken up and a grant proposal intended to pursue it was rejected at an early stage. In this paper, we revisit this idea as a case of an overlooked research direction. We reconstruct its argument, discuss why a published and cited idea failed to gain traction, and argue that recent developments in process mining, large language models, and AI agents make the agenda more relevant than before. Our wider aim is to ask what it means that the BPM community has paid so little attention to the artifact that arguably guides work in practice more than any other.

Internet-of-Things-enabled systems that monitor usage and inventory are the latest technological advancement in demand forecasting and inventory control. Unlike traditional systems that record sales via cash registers or RFID technology at the point-of-sale, these novel systems can track product usage via smart, connected devices at the point-of-consumption, i.e., directly at the end user. This usage data promises to be a valuable basis for smart, automated replenishment services. We study such a service in the context of commercial coffee machines through collaboration with a large manufacturer in the coffee industry. Our data set contains information on more than 75 million drinks recorded since late 2017 by nearly 6,500 IoT-enabled coffee machines for commercial customers such as office kitchens, restaurants, and gas stations. The nature of the problem and data at the point-of-consumption warrants the development of synergetic models for demand forecasting, inventory control, and correction of inventory record inaccuracy. The resulting models are distinct from the state-of-the-art approach at the point-of-sale as they are uniquely integrated and involve an alternative strategy to mitigate inventory record inaccuracies. Overall, we contrast different approaches to manage smart replenishment systems, test their forecasting, inventory control, and inaccuracy correction performance, and pave the path to implementation in the field. Our findings suggest important implications for manufacturers who wish to engage in direct relationships with the end users of their products.

Humanitarian supply chains in 2025 operated under a new kind of pressure. The crises were familiar—
protracted conflicts in Sudan, Gaza, and Ukraine; climate-driven disasters across the Indo-Pacific and the Sahel; displacement at record levels. But the
environment around the response hanged dramatically as donor funding contracted and the gap between needs and resources widened to historic proportions.
The central question became whether those gains could survive a funding squeeze. This 2025 annual global survey by CHORD—a partnership between Kühne Logistics University and HELP Logistics—gathered responses from 226 supply chain professionals across diverse humanitarian actors, regions, and operational levels. Nearly seven in ten
respondents work at the national level, in field and country offices, making these findings a window into
operational reality rather than institutional aspiration.

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.

The sociocultural perspective on social class holds that people from the working class (vs. middle and higher class) show more prosocial behavior because they have an interdependent self-construal (i.e., understanding the self as connected to others). This perspective, however, is challenged by numerous other studies that find that social class is positively related to prosocial behavior, arguing that prosocial behavior requires economic resources. Against this background, in an effort to integrate the disconnected sociocultural and economic perspectives on social class, we argue that both are true, but that (a) sociocultural and economic aspects of social class differently influence the extent to which people from the working class engage in prosocial behaviors, and that (b) these influences differ depending on the situation. Specifically, when directly interacting with someone in need, the interdependent self-construal of people from the working class prompts them to help, but when doing so involves monetary costs, limited economic resources constrain their ability to help. We present three complementary studies—a meta-analysis, an archival data analysis, and an experiment—to support our theorizing. Together, these findings provide an integrated picture of when and why social class is associated with prosocial behaviors.