All KLU Publications

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.

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.

The purpose of this study is to advance knowledge of the effects of using environmental criteria in the tendering and procurement of pharmaceuticals. In cooperation with the Norwegian Hospital Procurement Trust, we conducted a longitudinal case study of how environmental criteria in the tendering of generic antibiotics were implemented. This case is one of the first attempts to use environmental criteria in generic pharmaceuticals procurement. Qualitative and quantitative data were collected based on interviews, workshops, meetings and secondary sources, followed by iterative analysis, including a case narrative and causal loop diagramming. We found that rewarding environmental criteria fulfilment at the expense of price seems to have multiple effects. Firstly, higher prices increase public spending in the short-term but also help maintain the supply base, which can reduce shortages. Secondly, this increased focus gives more transparency into supplier risks. It is this latter unexpected effect of introducing environmental tendering criteria that makes the case and our analysis particularly interesting. Additionally, in the long term, interviewees expect that increased focus on environmental criteria for specific antibiotics will have the intended effect of reducing transmission of resistant bacteria through improving suppliers’ production processes. Our findings also suggest that increased focus on environmental criteria does not necessarily increase shortages of narrow-spectrum antibiotics, which could unintendedly increase transmission of resistant bacteria. Data from our case study suggest that these two effects are likely to have a positive influence on public health, thereby potentially reducing future healthcare costs.

In many real-world situations, multiple humans are involved in decision-making when interacting with machine recommendations. We investigated a setting where an artificial intelligence system creates demand forecasts that a human planner can either accept or revise, and a supervisor then makes the final decision about which forecast to select. We designed and conducted two experimental studies to understand decision-making by a supervisor. First, we provided the improvement probabilities of adjustments at an aggregated level and found evidence for overoptimism bias and mean anchoring. Second, we provided decomposed guidance based on two adjustment attributes, direction and magnitude, to investigate the role of salience based on the distance between the improvement probabilities and level of detail in guidance effectiveness. We found no significant difference in using less and more salient guidance provided that the detail level was fixed. However, revealing more details when the guidance was more salient increased the use of guidance.

This study advances and tests a micro-foundations model that reveals when and how corporate social responsibility (CSR) will enhance organizational innovation. Challenging the prevalent assumption that CSR uniformly leads to positive outcomes, we posit that the impact of CSR on innovation is contingent upon the interplay between employee-level psychological processes and organizational-level factors. Specifically, we argue that under conditions of good internal organizational communication, CSR facilitates employees' intrinsic motivation. Then, this motivation can increase organizational-level innovation, but only if employees are also allowed to thrive, when they are psychologically empowered. We examine the multi-level model by utilizing a 4-wave, time-lagged data from one of the largest Korean commercial banks, featuring 2545 employees across 379 branches. The data consist of both survey data and centrally audited CSR data. The results of the analyses bolster our hypotheses, but also highlight unexpected backlash effects where CSR negatively affects organizational innovation. Our findings contribute to the CSR literature by unveiling the complex micro-level mechanisms and boundary conditions that shape the CSR-innovation relationship, thereby addressing the inconsistencies in previous research. Practically, our study suggests that managers should carefully align their CSR initiatives with internal communication strategies and employee empowerment practices to foster innovation. Failing to do so may inadvertently undermine the very outcomes CSR is intended to promote. These insights also speak to the ongoing debate on the role of CSR in driving organizational competitiveness and social impact, underlining the need for a more nuanced and contextualized understanding of CSR's effects. In sum, our results facilitate the integration of previously disparate literatures, while simultaneously also underlining that CSR efforts need to be orchestrated with other improvements if any innovation benefits are to be reaped.

We investigate the nexus between the early-life disaster experiences of chief executive officers (CEOs) and their firms’ environmental performance metrics. We hypothesize that first-hand experience of the adversities of natural disasters in the formative years of a CEO can catalyze a transformation in their environmental cognizance and perspective. This transformation is postulated to have a beneficial influence on their corporations’ strategic frameworks for environmental risk mitigation. Our results show that entities steered by CEOs exposed to disasters in their early life have fewer incidences of environmental issues. These findings remain consistent even when controlling for other factors or using alternative methods. We suggest that CEOs with early disaster experience have an enhanced perception of risk ramifications, which inculcates a prudential approach to decision making, potentially heightening the environmental risk profile of their enterprises.

Studies have shown that anomie, that is, the perception that a society's leadership and social fabric are breaking down, is a central predictor of individuals' support for authoritarianism. However, causal evidence for this relationship is missing. Moreover, previous studies are ambiguous regarding the mediating mechanism and lack empirical tests for the same. Against this background, we derive a set of integrative hypotheses: First, we argue that perceptions of anomie lead to a perceived lack of political control. The repeated failure to exert control in the political sphere leads to feelings of uncertainty about the functioning and meaning of the political world. This uncertainty heightens people's susceptibility to authoritarianism because, we argue, the latter promises a sense of order, meaning, and the guidance of a "strong leader." We support our hypothesis in a large-scale field study with a representative sample of the German population (N = 1,504) while statistically ruling out alternative explanations. Adding internal validity, we provide causal evidence for each path in our sequential mediation hypothesis in three preregistered, controlled experiments (conducted in the United States, total N = 846). Our insights may support policymakers in addressing the negative political consequences of anomie. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Sustainable Aviation Fuel (SAF) is crucial for aviation decarbonization, but its current pre-blending process at refineries presents challenges, including fixed blending ratios, higher transportation costs and long lead times. This study explores the potential of an innovative technology that enables on-site SAF blending at airports. By postponing blending to the point of use, this approach offers customization opportunities. However, the precise benefits and trade-offs of this concept remain unclear. The research aims to assess the impact of on-site blending on fuel price, lead time, carbon emissions and supply chain costs.This empirical study evaluates the effects of SAF postponement using case analyses of Singapore-Seletar and Maastricht airports. The analysis incorporates cost modeling, lead time assessment and carbon impact calculations to quantify the implications of shifting blending downstream to airport sites. Data sources include industry reports, airport-specific logistics information and SAF supply chain parameters. A comparative analysis is conducted to determine optimal airport conditions for SAF postponement, highlighting key enablers and barriers to implementation.The results indicate that on-site SAF blending can create competitive advantages by reducing supply chain costs and lowering carbon emissions. The benefits are contingent on airport-specific factors, such as Hydroprocessed Esters and Fatty Acids availability, logistics infrastructure and regulatory conditions. The findings suggest that certain airports, particularly those with strategic locations and favorable cost structures, are better suited for adopting SAF postponement. By shifting production downstream, airports can achieve greater flexibility in SAF blending ratios while minimizing logistical inefficiencies.To the best of the authors’ knowledge, this study is among the first to empirically examine the feasibility of postponing SAF blending to the airport level. While existing literature focuses on SAF production and distribution, the concept of downstream blending has not been systematically analyzed. The research provides new insights into how mass customization principles can be applied to SAF supply chains, potentially reshaping fuel logistics in the aviation industry. By identifying critical factors for successful implementation, this study contributes to both academic discussions and practical decision-making in sustainable aviation fuel management.