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.

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.

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.

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.

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.

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.

Firms are exposed to varying levels of supply chain risk and engage in efforts to resolve such risk. This paper examines how disclosures of supply chain risk and resolution during earnings calls affect firms? stock returns. Using natural language processing, we develop measures of supply chain risk and resolution from quarterly earnings call transcripts for a total of 129,981 firm-quarter observations between 2008 and 2019. We find that higher levels of supply chain risk are associated with lower stock returns around earnings calls, while disclosures of supply chain risk resolution attenuate these negative effects. In particular, stock returns of firms in the highest supply chain risk quintile are 1.07% lower compared to the stock returns of firms in the lowest quintile, and regression analyses indicate that a one-standard-deviation increase in supply chain risk is associated with a 0.56% decline in stock returns. The stock returns of firms in the highest supply chain risk resolution quintile are 0.12% higher compared to the stock returns of firms in the lowest quintile. A one-standard-deviation increase in resolution increases stock returns by 0.08%, and to 0.29% for the subsample of observations where the resolution measure is positive. Exploratory analyses indicate that the effect of supply chain risk on stock returns is significantly greater for smaller firms than for larger firms. In addition, when there is evidence that larger firms? resolution-related statements are mere rhetoric, the effect of resolution on stock returns is diminished.

Movies are often sequentially offered through different distribution channels such as theaters, video sales and rentals, and online streaming. This research answers the question of how the advertising budget should be allocated across the various distribution channels to maximize overall profit. An extant allocation model is limited because of maximizing the logarithms of sales minus costs rather than true untransformed profit. Overcoming this limitation, my research offers a simple, near-optimal rule which takes into account not only the various effectiveness measures of advertising in terms of elasticities and, both, carryover and spillover effects, but also the size of the channels (expected profit) and truly maximizes profit. Due to its cascading property, the rule can be extended to any number of sequential distribution channels. This article also explains how the parameter values for the rule can be obtained and what profit improvement can be gained depending on the data structure.

Cognitive demands are increasingly prevalent in today’s complex work environments. With research having established that cognitive demands lead to strain, we introduce and test error management as a strain buffer for cognitive demands. We examined our theoretical model with two field studies. Across both studies, we found that when error management was low, cognitive demands were positively related to strain, while the relationship between cognitive demands and strain vanished when error management was high. This interaction was unique for cognitive demands, as error management did not influence strain in response to workload. Errors in cognitively demanding tasks were seen as more internal, but more controllable and less stable than errors when working with high workloads. Yet, we could not find error management influencing error attributions as we assumed to be the underlying theoretical mechanism. In sum, we suggest error management as a tangible mean by which organizations and employees can mitigate the strain-inducing effect of cognitive demands, which needs further research to be better understood.

User-generated content (UGC) is generally understood as an expression of opinion in many forms (e.g., complaints, online customer reviews, posts, testimonials) and data types (e.g., text, image, audio, video, or a combination thereof) that has been created and made available by users of websites, platforms, and apps on the Internet. In the digital age, huge amounts of UGC are available. Since UGC often reflects evaluations of brands, products, services, and technologies, many consumers rely on UGC to support and secure their purchasing and/or usage decisions. But UGC also has significant value for marketing managers. UGC allows them to easily gain insights into consumer attitudes, preferences, and behaviors. In this article, we review the literature on UGC-based decision support from this managerial perspective and look closely at relevant methods. In particular, we discuss how to collect and analyze various types of UGC from websites, platforms, and apps. Traditional data analysis and machine learning based on feature extraction methods as well as discriminative and generative deep learning methods are discussed. Selected use cases across various marketing management decision areas (such as customer/market selection, brand management, product/service quality management, new product/service development) are summarized. We provide researchers and practitioners with a comprehensive understanding of the current state of UGC data collection and analysis and help them to leverage this powerful resource effectively. Moreover, we shed light on potential applications in managerial decision support and identify research questions for further exploration.

Sustainability has become a critical concern of many societies worldwide. The need for a more sustainable mode of producing and consuming goods and services while balancing related environmental, social, and economic consequences (i.e., the triple bottom line) is evident. Although research offers insights into many aspects of this necessary transformation, little is known about the extent to which firms and consumers stress environmental, social, and economic sustainability in their communication. This research addresses these questions by conceptualizing the interplay between sustainability-related firm-generated and user-generated content as a signaling phenomenon. In addition, the authors develop a custom dictionary that enables researchers and practitioners to identify and analyze sustainability-related textual data. An illustrative application based on major data sources (corporate websites, Amazon, and YouTube) indicates significant divergence in how firms and consumers communicate about sustainability. Building on this first conceptual and empirical foray into sustainability-related firm-generated and user-generated content, this research outlines open research questions and potential use cases for the provided analytical tool.

In this research, we set out to uncover why silver ceilings exist in organizations. Drawing on systematic–heuristic processing theory and recent psychological findings, we propose that “older” workers (aged 45 or more) are less likely to receive promotions because these decisions are based on potential appraisals, which are susceptible to managers’ heuristic (stereotypical) thinking. We test our hypotheses using two-wave field data (Study 1) from a large financial organization and an experiment (Study 2) in which we manipulate age while holding all else equal. Both studies show that employee age has a negative effect on promotion likelihood and that this relationship is mediated by managers’ potential appraisals. Moreover, Study 2 also provides evidence for our theoretical rationale showing that the central effect is driven by managers’ heuristic processing and work-related age stereotypes. Across both studies, our results provide consistent support for our hypothesis that appraisals of potential constitute a potent pathway via which managers’ age stereotypes can affect promotion decisions in organizations. We discuss theoretical contributions to the literature on workplace aging, employee appraisals, and personnel decisions, and formulate practical recommendations to help organizations tackle silver ceilings in the workplace.

Global container shipping is integral to international trade, and a nuanced understanding of the role of strategic alliances and market concentration is crucial for the continuous and secure functioning of global logistics across different trades. We investigate the spatio-temporal evolution of alliance deployment and market concentration in the container shipping industry. This study introduces an innovative methodological approach - clustering trade routes using Dynamic Time Warping (DTW) based on alliance deployment and market concentration metrics rather than relying on predefined geographic boundaries. The approach uncovers previously unexplored structural relationships between alliance strategies and market dynamics, providing a more nuanced understanding of the container shipping industry's competitive landscape and potential vulnerabilities. We address important questions on how alliance deployment, market concentration, and inequality correlate or differ across global trade lanes and the implications for a potential threat of market power or collusive behavior for international trade and market accessibility. Our findings reveal that extensive alliance deployment does not inherently lead to a heightened market concentration or inequality. On major East-West trade routes, high levels of alliance deployment correspond with relatively low market concentration and inequality, indicating competitive environments where multiple carriers actively compete for market share. Conversely, niche markets exhibit higher market concentration and inequality, with increased potential for collusive behavior, especially where alliance deployment is minimal or absent. Our results underscore the need for regulatory bodies to foster fair competition, mitigate anti-competitive practices under a differentiated approach, and enhance market accessibility in the context of global trade flows. Finally, our research reveals the risk of power imbalances between regulators of small countries and leading global shipping lines.

The success of entertainment products such as movies or books varies tremendously, and managers strive to increase the odds by deciding on the right marketing input. Aiming to improve managerial decision making, we suggest and test a quantile regression framework to detect outcome heterogeneity effects of marketing inputs in the entertainment industry. By analyzing the spread of the .9 and the .1 conditioned quantile to the .5 (median) conditioned quantile, we study how much an increase (decrease) of an input factor (star power and quality) changes the spread of the expected outcome (revenues and sales). The spread serves as an indicator for the heterogeneity effect of the input factor regarding the outcome. In two empirical studies, we show how marketing instruments increase (or decrease) outcome heterogeneity by estimating quantile regressions and provide generalizable findings regarding the outcome heterogeneity effects of star power (increases outcome heterogeneity) and quality evaluations (reduces outcome heterogeneity) in the entertainment industry.

We study the role of inventory in corporate resilience to Covid-19 in 2020, which triggered exogenous shocks to consumer demand, commodity prices and supply chains. Unexpected drops in consumer demand and commodity prices increase the costs of inventory. Conversely, inventory holdings can buffer against supply disruptions. Empirically, US firms with higher inventory experienced more negative stock market responses early in the crisis due to falling consumer demand. However, since May 2020, inventory has become valuable as a hedge against supply disruptions, improving firm performance. During Covid-19, unlike other crises, inventory played a unique role as a hedge against supply disruptions.

Various entities, such as startups, suppliers and governments, face substantial difficulties in convincing nanostore shopkeepers to adopt digital technologies. Given the informal status of nanostores, we posit that shopkeepers experience Tax Privacy Concerns from their operational records potentially becoming transparent to the tax authorities, which hampers their inclination to digitize. Through the application of a survey and vignette experiments in the field with hundreds of shopkeepers across three cities in Latin America, we find consistent evidence for the negative role of Tax Privacy Concerns, above and beyond shopkeepers' willingness to share data with various entities, trust in the government and other entities, and general privacy concerns.
Further, we show that having entities that shopkeepers trust and are willing to share data with offer technological solutions does not mitigate shopkeepers' Tax Privacy Concerns and boosts digitization. In contrast, positive word of mouth that data are unlikely to be shared with the tax authorities does mitigate Tax Privacy Concerns. Overall, our findings provide novel evidence for the existence and influence of privacy concerns for operational data among microentrepreneurs, which answers calls in the extant literature to explore privacy concerns.beyond the consumer context.

Problem definition: Emergency medical services (EMS) in many low- and middle-income countries utilize decentralized platforms coordinating independent ambulance providers. However, significant operational challenges arise from uncertainty in provider time availability and unpredictable idle locations. These uncertainties hinder reliable service coverage and negatively impact patient outcomes. Using data from our partner Flare in East Africa regarding their operations in Nairobi, Kenya, we investigate the relative effectiveness of enhancing provider temporal commitment (time availability) versus spatial commitment (strategic location) to improve system coverage.

Methodology/results: We employ optimization models tailored to ambulance commitment uncertainty, a detailed case study, data-driven simulations, and a game-theoretic model. Our findings quantify a stark “cost of decentralization”: the coverage provided by Flare’s 340 loosely committed ambulances could potentially be matched by fewer than 15 optimally deployed fully committed units. We find that enhancing spatial commitment typically yields substantially larger coverage gains than increasing time availability alone, and that both dimensions are strongly complementary. Simulations validate these effects under stochastic demand, simultaneous calls, and strategic relocation. A stylized free-entry model, in which time- and spatially committed providers enter until profits vanish, shows that spatial commitment dominates except at very low temporal availability and that adding spatially committed units typically yields higher marginal value by filling geographic gaps. Together, all three approaches highlight the significant value of spatial coordination.

Managerial implications: For managers of decentralized EMS platforms, the main priority should be to enhance spatial coordination. This can be achieved by deploying a small number of platform-controlled, location-flexible units or by incentivizing providers to relocate strategically, both of which are especially effective at closing the system’s most critical coverage gaps. Because these benefits persist even when accounting for stochastic demand, simultaneous calls, and potential strategic behavior by independent providers, managers need not account for every contingency when evaluating interventions; focusing on closing persistent geographic gaps yields robust performance gains in resource-constrained settings.

This study introduces a simulation-based analysis of the decarbonization options for the road freight transport sector. It focuses on exploring the impact of operational and management measures on fleet renewal strategies aimed at achieving net zero goals by 2050. The proposed approach integrates current and planned future policy changes, operational practices, and technology renewal into the modeling process to offer a macro-level perspective on the decarbonization challenge. Specifically, the proposed modeling approach takes into account the reduction of empty trips, the optimization of cargo consolidation, and the promotion of eco-driving practices based on national freight transport data (i.e. covering more than 7.99 million trips). The proposed approach examines the effect of introducing contemporary vehicle technologies, such as new diesel vehicles (EURO VI or higher), new natural gas vehicles (EURO VI or higher), electric vehicles and hydrogen vehicles, as feasible replacements for aging vehicles powered by conventional fossil fuels. The adoption of these cleaner and newer technologies demonstrates the potential for emission reductions of up to 13% (2,070,000 tons CO2e) by 2030 and 47% (13,232,000 tons CO2e) by 2050. In addition, the results obtained from this research can serve as an exemplary case study for other emerging economies.

Despite extensive research streams on leadership and team processes, there is a surprising paucity of studies at their intersection. Both research streams share an increasing attention to the social interactions at the core of these phenomena. Leveraging this behavioral lens, this study draws on respectful inquiry theory to explore how specific leader communication behaviors affect team interaction dynamics during decision-making, as one important team process. We conducted a laboratory study with 22 four-person teams and a confederate leader who engaged in a hidden profile task in a personnel selection scenario. We manipulated the leader’s question asking behavior (open questions vs. statements only) and listening behavior (listening attentively vs. not listening) and randomly assigned teams to one of the four conditions. Team interactions were video-recorded and analyzed at the micro-level of communication. Specifically, we explored how leader communicative behaviors affected (1) the quality of team decision-making, (2) the conversational structure (via speaker turns), and (3) constructive communication patterns. We found that team’s yielded the lowest performance in the “disrespectful inquiry”-condition (i.e., asking questions but not listening). This condition was also characterized by increased levels of interaction amongst team members that could be interpreted as an attempt to compensate for the lack of functional leadership. By adopting a consistent, micro-level behavioral perspective, our findings bridge the literature of leadership and team interactions and suggest an update to extant theorizing on leadership substitutions.

Manufacturing firms face complex after-sales challenges, including spare part shortages. While additive manufacturing (AM) offers a solution by minimizing costs and complexity, not all firms adopt AM equally, and research on differences in AM adoption in the context of spare part shortages is surprisingly scarce. To close this knowledge gap, we apply the awareness-motivation-capability (AMC) perspective. Our comparative case study of AM applications in 17 firms identifies three approaches how firms adopt AM—the corrective, preventive, and anticipatory approach. We find that the specific configuration of contextual factors related to a spare part shortage determines the approach firms follow. Using the AMC perspective, we discover and explain why firms differ in adopting AM despite suitable spare part characteristics and similar contexts. Through uniquely analyzing spare part shortages, our study contributes to AM research by challenging the assumption that economic justification is the sole driver of AM adoption and instead revealing that it is a context-dependent process, with awareness and motivation serving as critical yet underexplored antecedents.

Many customers complain when informed that their order will not be fulfilled as originally confirmed, while other customers may be able to tolerate deviations. However, for suppliers, such complaints can be an early indicator of bad publicity, customer churn, and lost sales; and suppliers can prioritise orders to avoid these negative consequences. Ideally, they would know in advance if any order fulfilment change will trigger a customer complaint. To analyse how suppliers can predict these infrequent events in a business-to-business context, we leverage machine learning models on a large real-world dataset from a global semiconductor manufacturer. Our findings demonstrate that extreme gradient boosted trees effectively address the prediction problem. We explore the impact on model performance for different sampling approaches and cutoff values, as tuning the decision threshold is a meaningful calibration strategy before practical implementation. Our feature importance analysis provides evidence that high order fulfilment quality lowers complaint tendencies. Bridging the gap between advanced analytics and customer behaviour prediction, our research contributes to understanding the influence of subpar order fulfilment on customer satisfaction and offers insights into efficient order management despite disruptions. Our empirical study lays the groundwork for proactive supply chain operations when order fulfilment is at risk.

Demand uncertainty can lead to excess inventory holdings, capacity creation, emergency deliveries, and stock-outs. The costs of demand uncertainty may be directly borne by upstream suppliers, but can propagate downstream in the form of higher prices. To address these problems, we investigate a practical application of a fixed order commitment contract (FOCC) in which a manufacturer commits to a minimum fixed order quantity each period and receives a per unit price discount from the supplier for the commitment. We model a FOCC as a Stackelberg game in which the supplier offers a price discount anticipating the manufacturer’s response, and the manufacturer subsequently decides on the optimal commitment quantity. We show that a FOCC can smooth the orders received by the supplier, mitigating the negative consequences of demand uncertainty for the supplier, the manufacturer, and the supply chain. We extend the current literature by solving for an endogenous price discount instead of treating it as an exogenous value, and validate our model insights with our research partner, a large international materials handling equipment manufacturer. Using data on 863 parts, we evaluate the relationships between the model parameters, contract parameters, and the contract effectiveness, and show the conditions under which the FOCC generates greater cost savings for both the manufacturer and supplier. Our results help operations managers better understand how to obtain the optimal contract parameters for a FOCC and the circumstances under which such a contract is most beneficial for the company and its supply chain.