TY - JOUR A1 - Daschner, Stefan A1 - Obermaier, Robert T1 - Do We Use Relatively Bad (Algorithmic) Advice? The Effects of Performance Feedback and Advice Representation on Advice Usage JF - Journal of Behavioral Decision Making (ISSN: 1099-0771) N2 - Algorithms are capable of advising human decision‐makers in an increasing number of management accounting tasks such as business forecasts. Due to expected potential of these (intelligent) algorithms, there are growing research efforts to explore ways how to boost algorithmic advice usage in forecasting tasks. However, algorithmic advice can also be erroneous. Yet, the risk of using relatively bad advice is largely ignored in this research stream. Therefore, we conduct two online experiments to examine this risk of using relatively bad advice in a forecasting task. In Experiment 1, we examine the influence of performance feedback (revealing previous relative advice quality) and source of advice on advice usage in business forecasts. The results indicate that the provision of performance feedback increases subsequent advice usage but also the usage of subsequent relatively bad advice. In Experiment 2, we investigate whether advice representation, that is, displaying forecast intervals instead of a point estimate, helps to calibrate advice usage towards relative advice quality. The results suggest that advice representation might be a potential countermeasure to the usage of relatively bad advice. However, the effect of this antidote weakens when forecast intervals become less informative. KW - confidence intervals KW - forecast KW - perfect automation schema KW - performance feedback KW - relatively bad advice KW - source of advice Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-17615 SN - 0894-3257 SN - 1099-0771 VL - 37 IS - 5 PB - Wiley ER - TY - JOUR A1 - Vishnu Nampoothiri, Madhavan A1 - Entrop, Oliver A1 - Annamalai, Thillai Rajan T1 - Effect of mandatory sustainability performance disclosures on firm value: Evidence from listed European firms JF - Corporate Social Responsibility and Environmental Management (ISSN: 1535-3966) N2 - Corporate Sustainability Performance (CSP) reporting is becoming increasingly important to investors who seek to identify and invest in companies that are managing their Environmental, Social and Governance (ESG) risks effectively. The European Union's Non‐Financial Reporting Directive (NFRD), which was implemented in 2017, mandates that certain large companies must disclose their sustainability performance. This study examines the impact of the EU NFRD on the firm value of listed European firms using a difference‐in‐differences regression model. We find that the mandatory disclosure of corporate sustainability performance does not significantly affect firm value at an aggregate level. However, the results suggest minor inter‐industry differences, which can be attributed to varying sustainability performance metrics across industries. These findings contribute not only to the nascent literature on mandatory sustainability disclosures but also to the deliberations of policymakers and regulators across the world who are devising and implementing mandatory corporate sustainability performance disclosure regulations. KW - corporate social responsibility KW - ESG KW - firm value KW - mandatory disclosure KW - non‐financial reporting KW - sustainability Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-17620 SN - 1535-3958 SN - 1535-3966 VL - 31 IS - 6 SP - 5220 EP - 5235 PB - John Wiley & Sons, Inc. CY - Chichester, UK ER - TY - JOUR A1 - Donner, Eva Katharina A1 - Meißner, Annekatrin A1 - Bort, Suleika T1 - Moving from voluntary to mandatory sustainability reporting—Transparency in sustainable development goals (SDG) reporting: An analysis of Germany's largest MNCs JF - Business Ethics, the Environment & Responsibility (ISSN: 2694-6424) N2 - A growing number of multinational companies (MNCs) report on their progress toward contributing to the Sustainable Development Goals (SDGs) in their annual reports, yet the amount and quality of the information they disclose varies significantly. The aim of this study is twofold: First, we investigate how transparent MNCs report on their SDG engagement and second, we study how the reported SDG engagement changed over time due to major shifts in sustainability reporting requirements. Using a dataset of the largest German MNCs, we analyze their disclosure of SDG contribution reporting practices. Our results show that, overall, between 2016 and 2021 all German MNCs increased their SDG reporting activity and that the MNCs adopted four different practices to report their SDG engagement. We also found that the largest MNCs were more transparent and systematic in their SDG reporting than smaller MNCs, whose reporting was more heterogeneous. Our study contributes to legitimacy research and the role of transparency in corporate reporting as well as the debates on the efficacy of sustainability reporting legislation in the context of SDG engagement. Our findings imply that regulations on environmental and social regulation increase the transparency of sustainability reporting. KW - multinational companies (MNCs) KW - sustainability reporting KW - transparency KW - UN sustainable development goals Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-16527 SN - 2694-6416 SN - 2694-6424 VL - 34 IS - 3 SP - 900 EP - 911 PB - Wiley ER - TY - JOUR A1 - Goerigk, Marc A1 - Khosravi, Mohammad T1 - Robust combinatorial optimization problems under budgeted interdiction uncertainty JF - OR Spectrum (ISSN: 1436-6304) N2 - In robust combinatorial optimization, we would like to find a solution that performs well under all realizations of an uncertainty set of possible parameter values. How we model this uncertainty set has a decisive influence on the complexity of the corresponding robust problem. For this reason, budgeted uncertainty sets are often studied, as they enable us to decompose the robust problem into easier subproblems. We propose a variant of discrete budgeted uncertainty for cardinality-based constraints or objectives, where a weight vector is applied to the budget constraint. We show that while the adversarial problem can be solved in linear time, the robust problem becomes NP-hard and not approximable. We discuss different possibilities to model the robust problem and show experimentally that despite the hardness result, some models scale relatively well in the problem size. KW - - KW - Robust optimization KW - Combinatorial optimization KW - Budgeted uncertainty KW - Knapsack uncertainty Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:101:1-2408131120370.349068848723 SN - 0171-6468 SN - 1436-6304 VL - 47 IS - 1 SP - 255 EP - 285 PB - Springer CY - Berlin/Heidelberg ER - TY - JOUR A1 - Strunk, Kim Simon A1 - Strich, Franz T1 - Building professional holding environments for crowd work job crafting through online communities JF - Information Systems Journal N2 - Work is increasingly being organised via online platforms outside guiding organisational structures. Instead of having colleagues at work, crowd workers connect in online communities. We investigate how crowd workers build professional holding environments in online communities to compensate for the lack of organisational structures and we consider how they craft their crowd work activities to enhance their work experience and reduce its long-term precarity. Following a qualitative research design, this paper uses 675 forum interactions collected across six online communities. Based on our findings, we propose the concept of professional holding environments and provide a model for building such holding environments and job crafting in online communities. We thereby expand previous research on holding environments comprised of family members and friends by revealing the impact of professional online communities and their role in professionalisation and crafting supportive social structures in online crowd work. KW - crowd work KW - holding environments KW - job crafting KW - online communities Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-16315 VL - 33 SP - 1239 EP - 1274 PB - Wiley CY - Hoboken ER - TY - THES A1 - Grabl, Susanne T1 - How Technological Advancements and Digitalization Impact Both Organizational Behavior and Work Structures N2 - Organizations are in a constant state of flux, making transformations an inevitable aspect of their evolution and adaptation (By, 2005; Kotter, 1995). Transformations in organizations take many forms. These include sustainability transformations intended to make organizations more sustainable and fit for the future (Bansal, 2005; Corbett & Mellouli, 2017; Kim et al., 2017; Robertson & Barling, 2013). Additionally, transformations can affect day-to-day work and impact working practices (Orlikowski, 2002; Watson-Manheim et al., 2002, 2012). On the one hand, this enables more flexible working conditions through the introduction of hybrid work, for instance (Halford, 2005). On the other hand, new digital transformations, such as platform work (Deng et al., 2016; Deng & Joshi, 2016; Kittur et al., 2013; Kost et al., 2018), allow organizations to outsource work. Platform work allows users to work completely flexibly and organize their working day according to their needs (Deng et al., 2016; Deng & Joshi, 2016; Kost et al., 2018). A distinction can be made between different types of platforms. There are platforms that require physical labor, such as Uber (Duggan et al., 2020; Möhlmann et al., 2021), where transportation trips can be booked, or DoorDash, where food is delivered (Griesbach et al., 2019). Furthermore, there are entirely virtual online platforms. These include freelancing platforms such as Topcoder or Upwork, where workers advertise their profiles for jobs such as web design (Howcroft & Bergvall-Kåreborn, 2019; Shafiei Gol et al., 2019; Taylor & Joshi, 2019). They are evaluated by their clients and paid according to their work performance. There is also another type of platform with very low entry barriers: micro-task crowdsourcing platforms (Bush & Balven, 2021; Deng et al., 2016; Deng & Joshi, 2016; Wong et al., 2020). Here, people can perform currently posted tasks without prior application and receive a small remuneration (Deng et al., 2016). Digital transformations in the workplace change work processes and practices in the long term (Carroll et al., 2023). For instance, the introduction of hybrid work allows employees to divide their working time between the office and remote locations (Halford, 2005). Studies have shown that this shift has not negatively impacted employee performance, but has actually improved it (Abdullah et al., 2020; Mann & Holdsworth, 2003; Wessel et al., 2021). Thanks to the gain in flexibility, employees are at least as productive as in mere office work. However, this can also have negative effects, for example on the training of new employees or on maintaining social contacts among colleagues (Hafermalz & Riemer, 2020; Weritz et al., 2022). Another transformation changing organizations, particularly day-to-day work, is the introduction of artificial intelligence (AI) (Strich et al., 2021). AI encompasses a range of technologies that aim to mimic human cognitive processes or learn independently to solve problems (Asatiani, Malo, et al., 2021; Benbya et al., 2021; Berente et al., 2021). AI differs from previous technologies in terms of its self-learning nature, its (partial) autonomy in decision-making, and its opacity (Asatiani, Malo, et al., 2021; Barredo Arrieta et al., 2020). A special type of AI in use is generative AI (GAI). GAI is a model capable of creating apparently new content such as texts, codes, images, etc. (Brynjolfsson et al., 2023; Susarla et al., 2023). GAI applications like ChatGPT and Bard have quickly gained a large user base due to their easy accessibility, applicability, and versatility (Alavi et al., 2024; Kowalczyk et al., 2023). In addition to private use, GAI is utilized in various work processes across different professions (Benbya et al., 2024; Brynjolfsson et al., 2023), benefiting employees and organizations by improving the quality and efficiency of work (Brynjolfsson et al., 2023; Jia et al., 2024). In my dissertation, I explore these various types of transformation. Thereby, I contribute to a dynamic field and demonstrate how transformations in organizations can have a lasting impact on work processes. I have approached this multifaceted topic in a total of four studies. KW - Transformation KW - Work Structures KW - Organizational Behavior Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-15149 ER - TY - THES A1 - Sleziona, Philipp T1 - Transparency and Control about Privacy in Data-Driven Online Services: An Investigation of Design Choices and Users' Data Disclosure Decision-Making N2 - As modern online services are increasingly data-driven, users’ data disclosure is becoming a decisive factor for the success of companies' business models. Extensive disclosure of personal data raises concerns for users’ privacy that managers of online services and regulators need to address. Implementing transparency and control about an online service’s data practices has become the dominant approach to address these concerns. However, the effects of transparency and control on users’ data disclosure behavior have been found to be ambivalent. While some studies in the field of privacy research confirm that implementing transparency and control features can be an effective means to raise users’ willingness to disclose data, other studies have found only minuscule, and sometimes even opposing effects of transparency and control on users’ disclosure behavior. Such heterogeneous effects not only hinder estimating the consequences of regulatory and managerial efforts aimed at creating transparency and control but also impede generating clear guidelines on how to provide transparency and control in the most effective manner. A closer examination of individuals’ underlying decision-making mechanisms in response to provisions of transparency and control in disclosure decision scenarios may help to improve our understanding of the causes of diverging disclosure behavior. A better understanding of these underlying decisionmaking mechanisms will further help to assess specific designs of transparency and control features and their impact on the business models of online services as well as users’ privacy. Therefore, this thesis explores users’ decision-making mechanisms and behavioral reactions to the provision of transparency and control in data disclosure situations. The thesis consists of four essays that employ different research methods, including systematic literature reviews and quantitative studies. The findings of the essays contribute to a better understanding of the effects of transparency and control on user decision-making in the privacy domain by (1) offering conceptualizations that provide a lens for studying users’ decision-making processes and their disclosure behavior in reaction to transparency and control features (2) examining how different implementations of transparency and control features affect users’ decision-making processes and effort in decision-making which causes differences in users’ disclosure behavior, and (3) by investigating the effects of transparently communicated content and relevant pieces of information in so far underexplored data disclosure scenarios between users and a data-sharing firm network. KW - Transparency KW - Control KW - Privacy KW - Transparenz KW - Kontrolle KW - Privatsphäre KW - Datenschutz Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-15501 ER - TY - THES A1 - Stoffels, Dominik T1 - Advancing Pattern Detection, Theory Development and Decision Making with Explainable AI N2 - The application of explainable artificial intelligence (XAI) methods in data-driven decision-making and computationally intensive theory development (CTD) is a subject of ongoing debate, particularly concerning how and whether these methods can be effectively employed, and how the reliability of their explanations can be ensured. This dissertation addresses these issues by systematically analyzing the usability of XAI for pattern detection, CTD, and decision-making, drawing on various real-world and synthetic datasets and employing different empirical methods and perspectives. The dissertation consists of four studies, each addressing distinct issues in the field of XAI application. KW - Explainable Artificial Intelligence KW - Machine Learning KW - Computationally Intensive Theory Development Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-15975 ER - TY - THES A1 - Baak, Werner T1 - Advanced Ordered Weighted Averaging Methods in Robust Optimization N2 - In decision-making under uncertainty, robust optimization is a critical tool across various fields, providing solutions that perform effectively across a range of scenarios where precise probabilities are unavailable or unreliable. Traditional approaches, such as min-max and min-max regret, focus on minimizing the worst-case outcomes and worst-case regret, respectively, often resulting in highly conservative solutions. To address this limitation, this dissertation investigates the Ordered Weighted Averaging (OWA) operator, which offers a flexible framework for aggregating outcomes according to varying risk preferences, from risk-averse to risk-neutral, encompassing traditional robust approaches as special cases. This work is organized around three primary contributions that expand the application and understanding of OWA in robust optimization. The first contribution develops a preference elicitation framework for OWA weights, enabling decision-makers to derive weighting schemes based on observed historical decisions, thereby aligning aggregation strategies with specific risk attitudes. The second contribution introduces a novel variant of OWA for robust optimization, integrating OWA into a regret minimization framework to generalize both robust min-max and min-max regret approaches. This model is complemented by new complexity results, including insights into the inapproximability and approximability of OWA regret, providing stronger approximation bounds that asymptotically improve on previously established results for classic OWA models. These advancements position the OWA regret model as a powerful alternative to min-max regret, offering a more adaptable approach to risk-sensitive decision-making. The third contribution addresses interval uncertainty, extending the OWA framework to scenarios where outcomes are represented as bounded intervals instead of discrete points. This interval-based OWA model accommodates real-world decision-making needs, where scenario data are uncertain or costly to specify. By using Value-at-Risk (VaR) in our definition, we provide a natural way to handle continuous ranges of uncertainty while maintaining computational tractability for large-scale problems. Together, these contributions advance both the theoretical and practical applications of OWA in decision making, establishing OWA-based methods as versatile tools for addressing complex uncertainties across a variety of decision-making environments. KW - decision-making KW - uncertainty KW - risk Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-15879 ER - TY - THES A1 - Lorenz, Catherine T1 - Studies on optimization problems with dynamically arriving information N2 - In today’s fast-paced world, transportation planning, e-commerce, smart manufacturing, emergency services, and financial markets operate in real-time environments where dynamically arriving information must be integrated on-the-fly into decision-making. Research produced online algorithms ranging from myopic reoptimization (Reopt) to learning-based anticipation methods. However, given the complexity of real-world problems, optimal decision policies remain unknown, and effectiveness is often assessed through simulations against simple benchmarks, leaving improvement potential and robustness uncertain. This dissertation proposes effective online policies using classical and innovative analytical and computational evaluation methods, establishing performance bounds and comparisons to optimal solutions. It designs algorithmic frameworks for two dynamic optimization problems: the Online Order Batching, Sequencing, and picker Routing Problem (OOBSRP) in warehousing and the Traveling Salesman Problem with a Truck and a Drone under Incomplete Information (TSP-DI), for disaster relief. Given the importance of automation in real-time environments, a strong emphasis is placed on robotic solutions. For the OOBSRP with manual and robotic carts, we prove that Reopt is asymptotically optimal with probability one under broad stochastic conditions. From a worst-case perspective, no policy can improve Reopt by more than 50%, as it is shown to be asymptotically two-competitive. A computational study confirms that Reopt’s gaps to the complete-information optimum are small, e.g. averaging less than 5% for a cost-minimization objective. A pattern analysis of Complete-Information Optimal Solutions (CIOSs), generated with dynamic programming algorithms, identifies simple algorithmic enhancements – like eliminating waiting, intervention, or strategic relocation – that further reduce costs and delivery times. These findings suggest limited benefits of anticipatory (including AI-based) algorithms in OOBSRP. Conversely, for TSP-DI, where road blockages reveal dynamically, Reopt performs poorly in the worst case, as we reveal its exponentially growing competitive ratio. We show that policies delaying deliveries for drone surveillance are significantly superior in competitive ratio. A proposed hybrid policy achieves best average and worst-case results in experiments. Using battery-limited drones introduces a challenging static subproblem within these policies, classified as Drone Routing Problems with Energy Replenishment (DRP-E). We develop a Very Large-Scale Neighborhood Search (VLNS) and an exact method for generic DRP-Es. VLNS searches an exponential-sized neighborhood of a promising solution entirely in polynomial runtime, making it ideal for real-time policies or intensification in metaheuristics. This dissertation underscores the importance of analytical guarantees and comparisons to the optimum in online algorithm design, as policy effectiveness often diverges from intuition and varies significantly across problems. KW - Dynamic optimization KW - Online algorithms KW - Competitive analysis KW - Probabilistic performance guarantees KW - Very large-scale neighborhood search Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-15649 N1 - According to § 11 FPromO, Abs. 1, Satz 5 of the Promotionsordnung and with the agreement of the Chair of the Board of Examiners for Doctoral Awards, the following minor revisions have been made for the publication of the dissertation compared to the version submitted for grading. These changes result from comments and requests by the external reviewer, Prof. Dr. Stefan Irnich, as well as the author’s own observations during the revision process: - Corrected minor typos in grammar and mathematical notation. - Implemented wording improvements. - Reorganized and updated the list of abbreviations alphabetically. - Updated the publication status of the list of papers to the submission date and added the affiliation of the University of Bologna. - Page 13: Corrected the reduction of the average observed gap to CIOS of Reopt (16.6 percentage points) and provided clarification. - Pages 23 and 58: Added an inequality of indices k and l in definitions of a partition of batches in an optimal solution. - Page 24, "because the cart- and picker equipment is order-specific for each batch" changed to "because of pick-lists that are printed out " - Page 55: Added and corrected a statement (one sentence) regarding the reference Wahlen and Geschwind (2023). - Page 71: Added the statement: "Note that an increase in batching capacity significantly impacts the runtime of the DP approaches for both objectives." - Page 80: Replaced and corrected Figure 3.8 (statement remains unchanged). - Page 79: Corrected column names in Table 3.12. - Standardized the abbreviation "VLNS" instead of "VLSN" throughout Chapter 5. - Unified the written-out problem names of OOBSRP and OBSRP-R throughout the dissertation ER - TY - JOUR A1 - Komander, Verena A1 - König, Andreas T1 - Organizations on stage: organizational research and the performing arts JF - Management Review Quarterly N2 - Management and organization scholars have long been intrigued by the performing arts—music, theater, and dance—as a rich context for studying organizational phenomena. Indeed, a plethora of studies suggest that the performing arts are more than an interesting sideline for authors, as they offer unique theoretical and empirical lenses for organization studies. However, this stream of literature spreads across multiple research areas, varies with regard to its underlying theories and methods, and fails to pay sufficient attention to the contextuality of the findings. We address the resulting limitations by identifying and reviewing 89 articles on management and organization related to the performing arts published in 15 top-tier journals between 1976 and 2022. We find that research in the performing arts advances organizational theory and the understanding of organizational phenomena in four key ways, namely by studying (1) organizational phenomena in performing-arts contexts; (2) performing-arts phenomena in organizational contexts; (3) organizational phenomena through the prism of performing-arts theories; and (4) organizational phenomena through the prism of performing-arts practices. We also find that, in contrast to other settings, the performing arts are uniquely suited for immersive participant-observer research and for generating genuine insights into fundamental organizational structures and processes that are generic conditions of the performing arts and management alike, such as leadership, innovation, and the management of uncertainty. Finally, based on our consolidation of the research gaps and limitations of the reviewed studies, we develop a comprehensive agenda for future research. KW - Performing arts KW - Music KW - Dance KW - Jazz KW - Theatre KW - Organization KW - Management KW - Leadership Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:101:1-2023010321004612876788 VL - 2024 IS - 74 SP - 303 EP - 352 PB - Springer Nature CY - Berlin ER - TY - JOUR A1 - Schweikl, Stefan A1 - Obermaier, Robert T1 - Lost in translation: IT business value research and resource complementarity : an integrative framework, shortcomings and future research directions JF - Management Review Quarterly N2 - Despite longstanding research efforts, there is still ambiguity surrounding the business value created by IT. To approach this conundrum, research focus has progressed from an isolated investigation of IT to the assessment of complementarity between IT and different non-IT resources such as work practices or decision structures. However, incoherence around the characteristics and scope of these complementary non-IT resources has created a fragmented body of research, preventing a sustainable knowledge creation. Thus, in this paper we synthesize the dispersed research efforts, identify shortcomings in the extant literature, and derive opportunities for future research. Specifically, we present a converging definition of complementary non-IT resources and specify their role in the value creation process from IT by viewing it through three distinct lenses: microeconomic theory, resource-based view, and contingency theory. We structure current research efforts by organizing complementary non-IT resources into distinct categories, namely strategy, structure, practices, processes, and culture (organizational resources), top management support, internal relations, and external relations (relational resources), worker skill (non-IT human resources), non-IT physical resources, as well as internal funds and external funds (financial resources). Finally, we highlight five important shortcomings in the current literature, such as the predominant use of reductionist approaches or monolithic IT measures, and make actionable recommendations to resolve them. KW - Literature review KW - Classification KW - IT value KW - Complementarity KW - Resource system KW - Configurations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:101:1-2022093021520340946532 VL - 2022 IS - 73 SP - 1713 EP - 1749 PB - Springer Nature CY - Berlin ER - TY - JOUR A1 - Grimm, Michael A1 - Luck, Nathalie A1 - Steinhübel, Franziska T1 - Consumers' willingness to pay for organic rice: Insights from a non‐hypothetical experiment in Indonesia JF - Australian Journal of Agricultural and Resource Economics N2 - As in many high‐income countries, there is increasing awareness towards organic farming in many low‐ and middle‐income countries. Sustained local demand is an essential requirement for further adoption of organic farming by smallholders, who typically have only limited access to export markets. Until now, only few studies have explored the local willingness to pay (WTP) for organic products in low‐ and middle‐income countries in real purchase situations. This paper analyses the consumers' WTP for organic rice in urban and suburban Indonesia using an incentive‐compatible auction based on the Becker–DeGroot–Marschak (BDM) approach. We further study the effect of income and a randomised information treatment about the benefits of organic food on respondents' WTP. Estimates suggest that respondents are willing to pay an average price premium of 20% compared with what they paid for conventional rice outside our experiment. However, our results also indicate that raising consumers' WTP further is complex. Showing participants a video about health or, alternatively, environmental benefits of organic food was not effective in further raising WTP. The results can be used as a basis for the design of alternative awareness measures to increase knowledge, interest and demand for organic food. KW - BDM KW - Indonesia KW - organic food KW - rice KW - willingness to pay Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-12412 VL - 67 IS - 1 SP - 83 EP - 103 PB - Wiley CY - Hoboken ER - TY - THES A1 - Faltermaier, Stefan T1 - A Sociotechnical Perspective on Digital Transformation of Work in Organizations, on Platforms, and in Academia N2 - The findings of this dissertation reveal that digital transformation in the workplace accelerates ongoing changes in work practices, leading to transformations in organizations on both social and technological levels (study one). A key challenge identified in our case from study one is that highly virtual and digital work environments can undermine the integration of new employees and weaken organizational cohesion, resulting in more isolated work that tends to occur either individually or in small groups. In completely detached work settings (study two), our findings emphasize the prevalence of negative work experiences - frustration in our case – and the central role of both social and technical antecedents. Work settings devoid of any organizational framework, such as microtask crowd work, show an even greater detachment from shared values and structures compared to study one, posing significant challenges for the workers involved. These challenging conditions for individuals and organizations highlight the necessity for further research and the development of innovative theories to better comprehend these dynamics, and to devise strategies to manage these challenges effectively. Studies three and four build on this premise. In these studies, within the context of the digital transformation of academic work, we demonstrated how ML combined with XAI applications can be employed in theory development. KW - Digital Transformation of Work KW - Hybrid Work KW - Online Labor Platforms KW - Artificial Intelligence KW - Explainable Artificial Intelligence Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-15233 ER - TY - THES A1 - Sawhney, Udit T1 - Incentivizing Sustainable Agriculture in Indonesia: Empirical Essays on the Role of Social Norms and Information Provision N2 - The Green Revolution (GR) was one of the most transformative events in modern agricultural history. It was characterized by the widespread adoption of high-yield crop varieties, synthetic fertilizers and pesticides, and advanced irrigation systems. By significantly increasing agricultural productivity, the GR responded to global food shortages and hence played a crucial role in educing hunger, particularly in developing economies. However, while the GR alleviated food insecurity and stimulated economic development, it also brought about several unintended consequences like environmental degradation, as well as, widening socio-economic inequalities. Farmers, particularly in South and Southeast Asia, indulged heavily in fertilizer overapplication, which led to declining soil health and long-term sustainability concerns. Recognizing these challenges, the United Nations’ Sustainable Development Goals (SDGs) placed significant emphasis on sustainable agriculture, particularly through SDG - 2, which seeks to end hunger and promote sustainable food production. In line with these efforts, there has been a growing movement by several countries and international organizations towards promoting sustainable agricultural practices that balances agricultural productivity with environmental conservation. This dissertation contributes to this discourse on sustainable agriculture by examining key socio-economic factors that influence the adoption of sustainable farming practices among smallholder farmers in Indonesia. Indonesia’s agricultural landscape, particularly its rice farming sector, has been shaped by decades of Green Revolution policies, with Java serving as a focal point for agricultural intensification. While these policies led to impressive yield increases and national self-sufficiency in rice production by the mid-1980s, they also resulted in several environmental and economic challenges, including excessive use of chemical fertilizers, soil nutrient imbalances, and long-term land degradation. In response, the Indonesian government introduced several sustainability-focused policies, including Integrated Pest Management (IPM), Farmer Field Schools (FFS), and the “Go Organic 2010” initiative. Despite these efforts, adoption of sustainable farming practices remains limited, raising critical questions about the barriers that prevent smallholder farmers from transitioning away from intensive chemical input use. The dissertation focuses on three interrelated research questions that explore the role of social networks, information provision, and economic incentives in shaping farmers’ decisions regarding sustainable agriculture. These questions are addressed through a combination of mixed-methods research and randomized controlled trials (RCTs) conducted in Java, specifically in the regions of Yogyakarta and Tasikmalaya. The first research question investigates whether and how social networks and peer effects influence farmers’ input decisions, particularly regarding fertilizer application. This study builds on existing literature on social networks in agriculture and examines the extent to which perceptions of farming norms - the visible greenness levels of rice plants - affect farmers’ willingness to adopt more sustainable practices. Using a mixed-methods approach, including survey experiments and social network analysis, the study finds that personal opinions about the importance of plant greenness significantly influence farmers’ input decisions. However, second-order perceptions - farmers’ beliefs about how others in their farming network think about their farming choices - do not play a decisive role in shaping actual adoption behaviour. This finding contrasts with previous studies that emphasize the role of social pressure in agricultural decision-making, suggesting that while social learning plays an important role, it does not always operate through peer-effect mechanisms. The results highlight the complexity of social influences in agricultural adoption decisions and the need for more nuanced approaches to integrating behavioural insights into policy interventions. The second research question investigates the role of information provision, particularly about site-specific nutrition, in promoting sustainable soil management practices among smallholder farmers. A large-scale RCT was conducted in 69 villages to assess whether targeted agricultural extension trainings, along with soil testing services, can drive farmers’ adoption of sustainable soil management practices. Villages were randomly assigned to - a treatment group (T1) that received one-day training on soil health management, a second treatment group that received both training and soil tests (T2), and a control group. The study reveals that while training sessions increased awareness and adoption of simple sustainable practices - such as the use of the Leaf Colour Chart (LCC) - there was limited impact on broader behavioural changes, such as the adoption of organic fertilizers or precision fertilizer application. However, the additional provision of soil testing led to measurable reductions in nitrogen fertilizer use while simultaneously increasing yields, demonstrating the potential for personalized, site-specific soil nutrient recommendations to improve both economic and environmental outcomes. A cost-benefit analysis reveals that additional day of soil testing training resulted in an average economic gain of USD 15.71 per farmer and also reduced CO2 emissions by approximately 2 kg per farmer. These findings highlight the potential for scalable, information-based interventions as well as the need for sustained follow-up support to reinforce behavioural changes among farmers. The third research question explores farmers’ willingness to pay (WTP) for soil testing services and compares two different market-dissemination models - a private service model (where farmers purchase individual soil tests) and a collective (club good) model (where farmer groups collectively purchase a soil testing kit). Using an incentive-compatible auction based on the Becker- eGroot-Marschak (BDM) method, the study finds that farmers are willing to pay approximately 43% of the actual cost of soil tests, indicating strong demand for personalized soil fertility information. Furthermore, there is no significant difference in WTP between the private and club good models, suggesting minimal free-riding behaviour within farmer groups. The qualitative data further suggests that group-based models foster a sense of joint responsibility and knowledge-sharing, making them a viable alternative to individual service provision. A deeper analysis reveals that while private service models are more effective in low-subsidy environments, club good models become preferable when subsidies are higher, offering valuable insights into cost-sharing mechanisms for agricultural policy design. Taken together, the findings of this dissertation have important implications for policymakers seeking to promote sustainable agricultural practices in developing economies. First, the dissertation highlights the nuanced role of social networks in shaping farmers’ adoption decisions, suggesting that interventions targeting social learning should account for the complexity of social networks and peer influence mechanisms. Second, the dissertation underscores the importance of integrating soil testing and personalized information into agricultural extension programs, as site-specific soil nutrient recommendations can enhance both farming productivity as well as environmental sustainability. Third, the study provides empirical evidence on cost-effective ways to scale up soil testing services, demonstrating that well- esigned market-dissemination strategies can increase farmers’ access to sustainability-enhancing technologies while maintaining financial viability. Beyond its immediate policy relevance, this dissertation also contributes to broader theoretical debates in development economics, agricultural economics, and environmental sustainability. By integrating experimental research methods, the dissertation advances understanding of how farmers make technology adoption decisions under conditions of uncertainty and social influence. Additionally, the study provides a methodological contribution by demonstrating the effectiveness of combining RCTs with qualitative approaches to capture the complexities of real-world decision-making. Despite its contributions, the dissertation also identifies several avenues for future research. One key limitation is that the analysis focuses primarily on short - to medium-term impacts, leaving open questions about the long-term sustainability of behaviour change. Future studies should explore whether farmers continue to adopt sustainable practices once external support is removed. Additionally, further research is needed to examine the role of digital agricultural advisory services, mobile-based soil testing platforms, and remote sensing technologies in complementing traditional extension services. Finally, more work is needed to explore the broader policy ecosystem surrounding agricultural sustainability, including the role of subsidies, market linkages, and certification schemes in incentivizing long-term adoption. In conclusion, this dissertation provides a comprehensive analysis of the social, informational, and economic factors that drive sustainable agricultural transitions in Indonesia. By offering evidence-based insights into the design of more effective extension programs, market dissemination strategies, and cost- haring mechanisms, it contributes to ongoing efforts to create more resilient and environmentally sustainable food systems. The findings are not only relevant for Indonesia but also offer valuable lessons for other developing economies that are facing the challenge of balancing agricultural productivity with sustainability. KW - Economics KW - Development Economics KW - Agricultural Economics KW - Randomized Controlled Trials Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-16095 ER - TY - JOUR A1 - Fritz, Manuela T1 - Wave after wave: determining the temporal lag in Covid-19 infections and deaths using spatial panel data from Germany JF - Journal of Spatial Econometrics N2 - The Covid-19 pandemic requires a continuous evaluation of whether current policies and measures taken are sufficient to protect vulnerable populations. One quantitative indicator of policy effectiveness and pandemic severity is the case fatality ratio, which relies on the lagged number of infections relative to current deaths. The appropriate length of the time lag to be used, however, is heavily debated. In this article, I contribute to this debate by determining the temporal lag between the number of infections and deaths using daily panel data from Germany’s 16 federal states. To account for the dynamic spatial spread of the virus, I rely on different spatial econometric models that allow not only to consider the infections in a given state but also spill over effects through infections in neighboring federal states. My results suggest that a wave of infections within a given state is followed by increasing death rates 12 days later. Yet, if the number of infections in other states rises, the number of death cases within that given state subsequently decreases. The results of this article contribute to the better understanding of the dynamic spatio-temporal spread of the virus in Germany, which is indispensable for the design of effective policy responses. KW - Covid-19 KW - Spatio-temporal models KW - Time lag effects KW - Spatial spillovers KW - Spatial Durbin model KW - Germany Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:101:1-2022112821095396799160 VL - 2022 IS - 3 PB - Springer Nature CY - Berlin ER - TY - THES A1 - Lehner, Constanze T1 - Three Essays on the Interpretability of Random Forests: Methods, Insights, and Innovations N2 - This thesis examines the interpretability of random forests in three essays, focusing on the discussion of established methods, the presentation of new insights and the provision of innovations. The research aims to bridge the gap between traditional statistical methods and random forests, a data-driven machine learning algorithm, by investigating the ability of random forests to adequately model theoretical concepts compared to parametric methods and developing a new approach for statistical hypothesis testing. The results have implications for various areas of research in which interpretability is crucial for applications. The next three paragraphs summarize the studies presented in this thesis. The ability of random forests to automatically model interactions without the need for pre-specification is mentioned prominently in many articles and book chapters. Promising empirical results from early work on random forests have substantiated this property, which has led to an increasing popularity of using random forests in the presence of interactions as an alternative to traditional parametric methods. This study reviews the literature of the last 20 years on random forests and interactions. We explore the discussion from its origin in the decision tree literature to early applications of random forests and current research. We identify key research areas and illustrate random forest applications to highlight similarities and differences between disciplines. We also provide a critical examination of the arguments in favor of random forests being able to model interactions automatically. Since the term ``interaction'' is associated with different theoretical concepts, we explain and illustrate the definition of interaction for each research area. The variable importance of random forests is an easy-to-understand metric that intends to make the predictions of random forests more transparent by assessing the contribution of each covariate to the prediction of the response. However, due to its data-driven nature, the variable importance of random forests may over- or underestimate the importance of a covariate, so that the role of the covariate in the underlying data generating process is not correctly reflected. We present an example of underestimation of importance in the case of interacting covariates. We define an interaction in terms of effect modification, which assumes that the effect of one covariate on the response is modified by values of another covariate. We show that the variable importance of random forests is influenced by the interaction form and the measurement scale of the interacting covariates, so that in some cases the importance of one or even both interacting covariates is underestimated. We illustrate how the split decisions of random forests affect the variable importance values of the interacting covariates. Variable importance estimates the contribution of a covariate to the performance of a predictive algorithm. Defined as the increase in loss after the random permutation of a covariate, permutation variable importance makes it possible to rank the covariates by importance, but in the absence of a threshold, the distinction between important and unimportant covariates is inherently arbitrary. We show that recent approaches of non-parametric permutation tests for variable importance exceed their nominal type I error level for mutually dependent covariates. As an alternative we propose a combined variable importance estimate on a sequence of permutations and employ a computationally more efficient bootstrap to derive the respective null distribution and $p$-values. The proposed test can be applied to any predictive algorithm and is remarkably fast. We investigate the control of type I error level and the power of the proposed variable importance test in simulation studies. Even for mutually dependent covariates, our test is conservative and provides power comparable to recent advances in non-parametric permutation tests of variable importance. This study was conducted in collaboration with Matthias Wild. KW - Random Forests KW - Interactions KW - Variable Importance KW - Permutation Test Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18608 ER - TY - JOUR A1 - Otto, Alena A1 - Golden, Bruce A1 - Lorenz, Catherine A1 - Luo, Yuchen A1 - Pesch, Erwin A1 - Rocha, Luis T1 - On delivery policies for a truck-and-drone tandem in disaster relief JF - IISE Transactions (Online ISSN: 2472-5862) N2 - This article introduces the traveling salesman problem with a truck and a drone under incomplete information (TSP-DI). TSP-DI is motivated by the deliveries of emergency supplies under unknown road conditions in the immediate aftermath of a disastrous event. The urgency may force the immediate dispatch of relief vehicles, such that road damages blocking the truck’s planned route are detected “on-the-fly”. The relief transport must schedule deliveries anticipating possible unplanned truck detours, enforce (planned) drone detours for early checking of key road segments, and consider the dynamic nature of road condition information. In this investigation, we perform a competitive analysis of a widely used delivery policy for TSP-DI in practice – the online re-optimization policy (Reopt) – and compare it with several alternative delivery strategies. Competitive analysis examines the worst-case performance of the strategies and is particularly important in the context of disaster relief, where worst-case outcomes must be avoided. Our analysis shows that Reopt is dominated by alternative delivery policies in terms of the competitive ratio even at a medium level of damage on the road. It also underscores the importance of surveillance detours performed by the drone, even if the surveillance delays the start of the deliveries. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18555 VL - 57 IS - 10 SP - 1198 EP - 1214 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Venus, Terese E. A1 - Beale, Caroline A1 - Villalba, Roberto T1 - Innovation and networks in the bioeconomy : a case study from the German Coffee Value Chain JF - Circular Economy and Sustainability N2 - The transition to a circular bioeconomy requires innovation across many sectors, but social dynamics within a sector’s network may affect innovation potential. We investigate how network dynamics relate to the perceptions and adoption of bioeconomy innovation using a case study from the food processing sector. Our case study of the German coffee value chain represents a technologically advanced sector with a strong sustainability focus and potential for residue valorization, which is an important dimension of a sustainable circular bioeconomy. We identify three distinct views (pioneers, traditional and limited users) related to residue valorization, map linkages between actors using social network analysis, and highlight barriers to innovation. We collected data through an online survey and semi-structured interviews with key actors in the coffee roasting sector. Within the social network analysis, we find that public waste managers are closely linked to the most influential actors, state actors such as the customs and tax offices can quickly interact with others in the network and promote the spread of information (highest closeness centrality) and specific roasters play an important role as intermediaries for efficient communication (highest betweenness centrality). Finally, we identify four main barriers including the structure of the coffee network, inconsistencies in federal waste regulations, economies of scale, and visions of sustainability. To support a sustainable bioeconomy, we recommend that policy makers revise the primary regulatory frameworks for waste (e.g., German Recycling Act) to clarify how to classify food residues, their disposal structures and broaden their use streams. KW - Bioeconomy KW - Coffee KW - Value chain KW - Germany KW - Social network analysis KW - Residue valorization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:101:1-2405070852079.655263471225 SN - 2730-597X SN - 2730-5988 VL - 4 IS - 3 SP - 1751 EP - 1772 PB - Springer International Publishing CY - Cham ER - TY - JOUR A1 - Goerigk, Marc A1 - Khosravi, Mohammad T1 - Benchmarking problems for robust discrete optimization JF - Computers & Operations Research (Online ISSN: 1873-765X) N2 - Robust discrete optimization is a highly active field of research where a plenitude of combinations between decision criteria, uncertainty sets and underlying nominal problems are considered. Usually, a robust problem becomes harder to solve than its nominal counterpart, even if it remains in the same complexity class. For this reason, specialized solution algorithms have been developed. To further drive the development of stronger solution algorithms and to facilitate the comparison between methods, a set of benchmark instances is necessary but so far missing. In this paper we propose a further step towards this goal by proposing several instance generation procedures for combinations of min–max, min–max regret, two-stage and recoverable robustness with interval, discrete, budgeted or ellipsoidal uncertainty sets. Besides sampling methods that go beyond the simple uniform sampling method that is the de-facto standard to produce instances, also optimization models to construct hard instances are considered. Using a selection problem for the nominal ground problem, we are able to generate instances that are several orders of magnitudes harder to solve than uniformly sampled instances when solving them with a general mixed-integer programming solver. All instances and generator codes are made available online. KW - Robust optimization KW - Benchmarking KW - Instance generator KW - Combinatorial optimization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18752 VL - 2024 IS - 166 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Nagel, Christian A1 - Heidenreich, Sven A1 - Schumann, Jan T1 - Enhancing adoption of sustainable product innovations : addressing reduced performance with risk-reducing product modifications JF - Journal of Business Research (Online ISSN: 1873-7978) N2 - Past studies have shown that the probability of the successful diffusion of sustainable product innovations is strikingly low. A potentially promising marketing strategy to reduce negative consumer perceptions of sustainable product innovations is risk-reducing product modifications (RPMs), which account for behavioral adjustments and performance uncertainties by employing an additional component that uses established technology. This research strives to empirically test the potential positive effects of RPMs using two studies on electric vehicles with and without range extenders as prime examples of RPM: (1) an online survey of potential adopters and (2) a postadoption survey of early adopters. The results show that RPMs increase the adoption of sustainable product innovations. Consumers’ tendency to overestimate their true needs and their need for insurance against insufficient performance explain the effectiveness of RPM. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18788 VL - 2024 IS - 179 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Otto, Alena A1 - Tilk, Christian T1 - Intelligent design of sensor networks for data-driven sensor maintenance at railways JF - Omega (Online ISSN: 1873-5274) N2 - With rapid advances in digitization, many critical processes in transportation, industries, and our daily life rely on sensor measurements. With time, however, the measurements may get gradually biased and their precision deteriorates, leading to an enhanced risk of major disruptions caused by false sensor measurements. All single sensor measurements are uncertain and deviate from the true value. To detect malfunctioning sensors early on, a set of recent measurements of each sensor has to be constantly cross-checked against the measurements of a given number of other sensors, i.e., sensors should form a diagnosable network. In this article, we examine the intelligent positioning of safety-relevant sensors at railways such that the installed sensors can constantly cross-check each other and the number of the required sensors is minimized. The arising sensor positioning problem (SPP) belongs to the family of the coordinated set covering problems with two binary matrices: the choice of columns in one matrix implies the selection of specific columns and rows in the other matrix. We formulate an integer program, provide some formal analysis of the SPP and design a customized large neighborhood search metaheuristic RuM, which finds close-to-optimality solutions fast. In our computational experiments, we show that if we ignore the diagnosability requirement, the installed sensors cannot sufficiently cross-check each other in most cases. However, it costs only a few (or even no) additional sensors to ensure the diagnosability of the sensor network. KW - Rail transport KW - Integer programming KW - Sensors KW - Set covering problem KW - Network design KW - Diagnosable network Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18771 VL - 2024 IS - 127 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Grimm, Michael A1 - Soubeiga, Sidiki A1 - Weber, Michael T1 - Supporting small firms in a fragile context : comparing matching and cash grants in Burkina Faso JF - Journal of Development Economics (Online ISSN: 1872-6089) N2 - We used a randomized controlled trial to compare matching grants earmarked for technical training and consulting services with more flexible cash grants and with a control group. The experiment was implemented in a semi-urban and rural fragile setting where subsidizing innovative activities might be particularly important. Firms were selected on the basis of a business plan competition. After two years, beneficiaries of cash grants showed higher survival rates, improved business practices, a higher degree of formalization, and more activities for innovation relative to recipients of matching grants and the control group, but we saw no effects on profits, sales, and employment. Across all outcomes, beneficiaries of cash grants performed better than beneficiaries of matching grants, for them the treatment effects are smaller and often insignificant, though implementation costs were higher. Recipients of cash grants also increased their capital stock more and were more resilient to the COVID-19 crisis. KW - Matching grants KW - Cash grants KW - Technical training and consulting services KW - Small firms KW - Business plan competition KW - Fragile countries Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18848 VL - 2024 IS - 171 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Fritsch, Markus A1 - Haupt, Harry A1 - Schnurbus, Joachim T1 - Efficiency of poll-based multi-period forecasting systems for German state elections JF - International Journal of Forecasting (Online ISSN: 1872-8200) N2 - Election polls are frequently employed to reflect voter sentiment with respect to a particular election (or fixed-event). Despite their widespread use as forecasts and inputs for predictive algorithms, there is substantial uncertainty regarding their efficiency. This uncertainty is amplified by judgment in the form of pollsters applying unpublished weighting schemes to ensure the representativeness of the sampled voters for the underlying population. Efficient forecasting systems incorporate past information instantly, which renders a given fixed-event unpredictable based on past information. This results in all sequential adjustments of the fixed-event forecasts across adjacent time periods (or forecast revisions) being martingale differences. This paper illustrates the theoretical conditions related to weak efficiency of fixed-event forecasting systems based on traditional least squares loss and asymmetrically weighted least absolute deviations (or quantile) loss. Weak efficiency of poll-based multi-period forecasting systems for all German federal state elections since the year 2000 is investigated. The inefficiency of almost all considered forecasting systems is documented and alternative explanations for the findings are discussed. KW - Fixed-event forecasting KW - Multiple lead times KW - Forecast efficiency KW - Weak efficiency concepts KW - Quantile loss KW - Election forecasting Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18793 VL - 41 (2025) IS - 2 SP - 670 EP - 688 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kleine, Jens A1 - Peschke, Thomas A1 - Wagner, Niklas T1 - Beyond financial wealth: The experienced utility of collectibles JF - The Quarterly Review of Economics and Finance (Online ISSN: 1878-4259) N2 - We argue that the utility of specific assets, in our case collectibles, is not only derived from the financial outcome, but also from the conditions that prevail until a financial outcome may be realized. Therefore, we derive a multi-attribute utility function that measures financial returns — using a mean-variance utility function — on the one hand, and non-financial returns — using an experienced utility function — on the other. We then reveal the trade-off between financial and non-financial utility by analyzing 363 owners of collectibles. We divide the owners into the group of collectors and the group of investors, based on their self-reported motivation. Our results suggest that collectors receive almost no utility from financial returns, but rather from experience. The opposite is the case for investors. Our findings help to explain the reported financial underperformance of collectibles and suggest to adjust existing models of utility. KW - Collectibles KW - Collecting KW - Decision utility KW - Event recall method KW - Experienced utility KW - Non-standard utility Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18808 VL - 2024 IS - 97 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Grüner, Alina A1 - Postel, Lea A1 - Schumann, Jan T1 - Sharing is caring? The effect of negative peer-to-peer experiences on loyalty intentions in the sharing economy JF - Journal of Business Research (Online ISSN: 1873-7978) N2 - Negative experiences with other users (i.e., negative peer-to-peer [P2P] experiences), are a highly prevalent problem of sharing economy business models with potentially negative consequences for the platform. This paper investigates the effect of negative P2P experiences on users’ platform loyalty intentions. Based on trust transfer theory, we test a moderated serial mediation suggesting that negative P2P experiences negatively affect platform loyalty intentions via a decrease in peer trust and a subsequent decrease in platform trust. The results of an online scenario-experiment (n = 265) and an online survey (n = 237) in the home-sharing context support this notion by showing a negative peers-to-platform trust transfer effect. In addition, we identified the type of negative experience (outcome-related vs. process-related) and prior relationship satisfaction as contingency factors. This paper draws platform providers’ attention to the potential threat stemming from negative experiences between users and provide advice on how to attenuate a negative spillover-effect on the platform. KW - Sharing economy KW - P2P KW - Loyalty KW - Trust transfer KW - Relationship satisfaction Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18820 VL - 2024 IS - 181 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bauer, Ida A1 - Haupt, Harry A1 - Linner, Stefan T1 - Pinball boosting of regression quantiles JF - Computational Statistics & Data Analysis (Online ISSN: 1872-7352) N2 - An algorithm for boosting regression quantiles using asymmetric least absolute deviations, better known as pinball loss, is proposed. Existing approaches for boosting regression quantiles are essentially equal to least squares boosting of regression means with the single difference that their working residuals are based on pinball loss. All steps of our boosting algorithm are embedded in the well-established framework of quantile regression, and its main components – sequential base learning, fitting, and updating – are based on consistent scoring rules for regression quantiles. The Monte Carlo simulations performed indicate that the pinball boosting algorithm is competitive with existing approaches for boosting regression quantiles in terms of estimation accuracy and variable selection, and that its application to the study of regression quantiles of hedonic price functions allows the estimation of previously infeasible high-dimensional specifications. KW - Quantile regression KW - Component-wise functional gradient boosting KW - Regression boosting KW - L1 loss KW - Consistent scoring rule Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18850 VL - 2024 IS - 200 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Fritsch, Markus A1 - Pua, Andrew Adrian A1 - Schnurbus, Joachim T1 - Teaching advanced topics in econometrics using introductory textbooks : the case of dynamic panel data methods JF - International Review of Economics Education (Online ISSN: 2352-4421) N2 - We show how to use the introductory econometrics textbook by Stock and Watson (2019) as a starting point for teaching and studying dynamic panel data methods. The materials are intended for undergraduate students taking their second econometrics course, undergraduate students in seminar-type courses, independent study courses, capstone, or thesis projects, and beginning graduate students in a research methods course. First, we distill the methodological core necessary to understand dynamic panel data methods. Second, we design an empirical and a theoretical case study to highlight the capabilities, downsides, and hazards of the method. The empirical case study is based on the cigarette demand example in Stock and Watson (2019) and illustrates that economic and methodological issues are interrelated. The theoretical case study shows how to evaluate current empirical practices from a theoretical standpoint. We designed both case studies to boost students’ confidence in working with technical material and to provide instructors with more opportunities to let students develop econometric thinking and to actively communicate with applied economists. Although we focus on Stock and Watson (2019) and the statistical software R, we also show how to modify the material for use with another introductory textbook by Wooldridge (2020) and Stata, and highlight some possible further pathways for instructors and students to reuse and extend our materials. KW - Teaching econometrics KW - instrumental variables KW - linear dynamic panel data methods KW - cigarette demand KW - lagged variables Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18835 VL - 2024 IS - 47 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Herkenhoff, Philipp A1 - Krautheim, Sebastian A1 - Sauré, Philip T1 - A simple model of buyer–seller networks in international trade JF - European Economic Review (Online ISSN: 1873-572X) N2 - The recent literature on firm-to-firm trade has documented salient empirical regularities of the buyer–seller network. We show that surprisingly many of these regularities emerge by superimposing the stochastic balls-and-bins structure of Armenter and Koren (2014) to firms in a classical Krugman (1980) model. Our approach amounts to a re-interpretation of Krugman (1980) and relies on randomized bundling of Krugman-varieties into heterogeneous firms, economically neutral ‘sales units’ that import foreign varieties but belong to local firms, and a statistical reporting threshold that applies to firm-to-firm transactions. We argue that our model provides an important benchmark for the assessment of theoretical models that aim to identify the determinants of firm-to-firm networks. KW - Firm-to-firm KW - Buyer–seller KW - Trade KW - Network KW - Random matching Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18949 VL - 2024 IS - 170 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Geschwind, Stephan A1 - Graf Lambsdorff, Johann T1 - Does scarcity induce hostility? An experimental investigation of common-pool resources JF - Ecological Economics (Online ISSN: 1873-6106) N2 - Climate change is leading to an increased scarcity of resources such as freshwater, energy, arable land and wildlife. This is perceived as a major security threat. However, the literature remains unclear on whether scarcity mitigates or exacerbates conflict. We design a novel laboratory experiment to investigate hostile behavior under scarcity. Participants interact repeatedly in a dynamic common-pool resource (CPR) and a joy-of-destruction game. The experiment distinguishes between two types of scarcity: Endogenous scarcity in the form of a deliberate human choice to overexploit resources and exogenous scarcity in the form of adverse environmental conditions. Our results show that endogenous scarcity exacerbates hostility. We trace this to participants being guided by negative reciprocity while finding no support for inequality aversion. The results indicate that to avoid hostility, policy makers will have to reduce human-induced scarcity. However, our results also show that exogenous scarcity mitigates hostility. This is in line with a vast body of literature from psychology, anthropology and biology finding increased levels of cooperation for all forms of life under environmental distress. It suggests that managing perceptions around increasing scarcities could be a second potential avenue for policy action. KW - Common-pool resources KW - Conflict KW - Laboratory experiment KW - Resource scarcity Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18958 VL - 2025 IS - 227 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bigler, Patrick A1 - Janzen, Benedikt T1 - Too hot to sleep JF - Journal of Environmental Economics and Management (Online ISSN: 1096-0449) KW - Climate change KW - Weather KW - Sleep Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18966 VL - 2024 IS - 128 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kinateder, Harald A1 - Gurrib, Ikhlaas A1 - Choudhury, Tonmoy T1 - Navigating crises: Gold's role as a safe haven for U.S. sectors JF - Finance Research Letters (Online ISSN: 1544-6131) N2 - This paper investigates the correlation between U.S. sectors and gold, and whether gold can serve as a safe haven for investors in specific U.S. sectors during the global financial crisis, COVID-19, and the Russia-Ukraine war. We use data from the Standard & Poor's Depository Receipts (SPDR) Select Sector Exchange Traded Fund (ETF) to capture the performance of the respective sectors. Our findings document that gold is a weak safe haven for most U.S. sectors. Gold is not a safe investment for energy, materials, utilities, and consumer staples. Gold does provide vital protection for financial, consumer discretionary, industrial, technology, and healthcare. KW - DCC-GARCH KW - Exchange-traded funds KW - Safe haven KW - U.S. sectors KW - COVID-19 KW - Global Financial Crisis KW - Russia-Ukraine war Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-18974 VL - 2024 IS - 69 B PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Daschner, Stefan A1 - Obermaier, Robert T1 - Algorithm aversion? On the influence of advice accuracy on trust in algorithmic advice JF - Journal of Decision Systems N2 - There is empirical evidence that decision makers show negative behaviours towards algorithmic advice compared to human advice, termed as algorithm aversion. Taking a trust theoretical perspective, this study broadens the quite monolithic view on behaviour to its cognitive antecedent: cognitive trust, i.e. trusting beliefs and trusting intentions. We examine initial trust (cognitive trust and behaviour) as well as its development after performance feedback by conducting an online experiment that asked participants to forecast the expected demand for a product. Advice accuracy was manipulated by ± 5 % relative to the participant’s initial forecasting accuracy determined in a pre-test. Results show that initial behaviour towards algorithmic advice is not influenced by cognitive trust. Furthermore, the decision maker’s initial forecasting accuracy indicates a threshold between near-perfect and bad advice. When advice accuracy is at this threshold, we observe behavioural algorithm appreciation, particularly due to higher trusting integrity beliefs in algorithmic advice. KW - Algorithm appreciation KW - cognitive trust KW - initial trust KW - Perfect Automation Scheme KW - trusting beliefs KW - Advice Accuracy KW - Forecasting Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-19120 VL - 31 (2022) IS - S1 SP - 77 EP - 97 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Stoffels, Dominik A1 - Faltermaier, Stefan A1 - Strunk, Kim Simon A1 - Fiedler, Marina T1 - Guiding computationally intensive theory development with explainable artificial intelligence: The case of shapley additive explanations JF - Journal of Information Technology (ISSN: 1466-4437) N2 - This study advances the field of Computationally Intensive Theory Development (CTD) by examining the capabilities of Explainable Artificial Intelligence (XAI), in particular SHapley Additive exPlanations (SHAP), for theory development, while providing guidelines for this process. We evaluate SHAP’s methodological abilities and develop a structured approach for using SHAP to harness insights from black-box predictive models. For this purpose, we leverage a dual-methodological approach. First, to assess SHAP’s capabilities in uncovering patterns that shape a phenomenon, we conduct a Monte-Carlo simulation study. Second, to illustrate and guide the theory development process with SHAP for CTD, we apply SHAP in a use-case using real-world data. Based on these analyses, we propose a stepwise uniform and replicable approach giving guidance that can benefit rigorous theory development and increase the traceability of the theorizing process. With our structured approach, we contribute to the use of XAI approaches in research and, by uncovering patterns in black-box prediction models, add to the ongoing search for next-generation theorizing methods in the field of Information Systems (IS). KW - - KW - computationally intensive theory development KW - next-generation theory development KW - machine learning patterns KW - IS research methods KW - explainable AI KW - black-box algorithms Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-16762 SN - 0268-3962 SN - 1466-4437 VL - 40 IS - 2 SP - 180 EP - 213 PB - SAGE Publications CY - London, England ER - TY - JOUR A1 - Drescher, Katharina A1 - Janzen, Benedikt T1 - When weather wounds workers : the impact of temperature on workplace accidents JF - Journal of Public Economics N2 - We study the effects of temperature on occupational health using administrative data on Swiss occupational accidents from 1996 to 2019. Our results imply that on hot days (T𝑚𝑎𝑥 ≥ 30 ◦C) the number of occupational accidents increases by 7.4% and on ice days (T𝑚𝑎𝑥 < 0 ◦C) by 6.3%, relative to mild days. We find that extreme temperatures cause an average of 2600 workplace accidents each year, costing CHF 91 million annually. We provide suggestive evidence for insufficient sleep on hot days as a mechanism. While extreme temperatures worsen occupational health, we observe limited labor supply adaption for most workers. KW - Occupational health KW - Labor supply KW - Climate change Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-19241 VL - 2025 IS - 241 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Goerigk, Marc A1 - Kurtz, Jannis T1 - Data-driven prediction of relevant scenarios for robust combinatorial optimization JF - Computers & Operations Research N2 - We study iterative constraint and variable generation methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. The goal of this work is to find a set of starting scenarios that provides strong lower bounds early in the process. To this end we define the Relevant Scenario Recognition Problem (RSRP) which finds the optimal choice of scenarios which maximizes the corresponding objective value. We show for classical and two-stage robust optimization that this problem can be solved in polynomial time if the number of selected scenarios is constant and NP-hard if it is part of the input. Furthermore, we derive a linear mixed-integer programming formulation for the problem in both cases. Since solving the RSRP is not possible in reasonable time, we propose a machine-learning-based heuristic to determine a good set of starting scenarios. To this end, we design a set of dimension-independent features, and train a Random Forest Classifier on already solved small-dimensional instances of the problem. Our experiments show that our method is able to improve the solution process even for larger instances than contained in the training set, and that predicting even a small number of good starting scenarios can considerably reduce the optimality gap. Additionally, our method provides a feature importance score which can give new insights into the role of scenario properties in robust optimization. KW - Robust optimization KW - Two-stage robust optimization KW - Data-driven optimization KW - Machine learning for optimization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-19207 SN - 1873-765X VL - 2025 IS - 174 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bouri, Elie A1 - Sokhanvar, Amin A1 - Kinateder, Harald A1 - Çiftçioğlu, Serhan T1 - Tech titans and crypto giants : mutual returns predictability and trading strategy implications JF - Journal of International Financial Markets, Institutions and Money N2 - This study examines the directional return predictability between the technology sector of U.S. stock market and three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin). Using daily data from August 7, 2015, to February 8, 2024, and the cross-quantilogram approach in both static and dynamic settings, the results reveal significant positive predictability in the stock market–cryptocurrency nexus. The technology sector, semiconductors subsector, and Nvidia Corporation exert predictive power over cryptocurrency returns and vice versa across several quantiles and lags. When controlling for the impact of other financial variables, namely, U.S. dollar and U.S. treasury markets, the return predictability holds, especially for the two largest cryptocurrencies, Bitcoin and Ethereum, which reflects their importance and tighter connections with the U.S. technology sector. A trading strategy based on the results of the cross-quantilograms outperforms a benchmark strategy (i.e., always long position in either stocks or cryptocurrency), which underlines the practical implications of our main findings, particularly in terms of the significant return interactions between U.S. technology/semiconductors stocks and large cryptocurrencies. KW - Bitcoin KW - Ethereum KW - Dogecoin KW - Nvidia KW - U.S. technology and semiconductor stocks KW - S&P500 index KW - Cross-quantilogram and return predictability across quantiles Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-19259 SN - 1873-0612 VL - 2025 IS - 99 PB - Elsevier CY - Amsterdam ER - TY - THES A1 - Grüner, Alina T1 - Stranger Danger? Three Essays on Consumer Trust and Data Disclosure in Multi-Actor Environments T1 - Stranger Danger? Drei Essays über Konsumentenvertrauen und Datenpreisgabe in Multi-Akteurs-Umgebungen N2 - Technological advancements and digitalization in recent decades have led an increasing number of firms to recognize the value of multi-actor business models. Integrating third parties into business processes provides firms with several benefits, including access to additional resources, skills, and information. However, from the consumer’s perspective, the involvement of third parties, often unknown to the consumer, is frequently associated with uncertainty, privacy concerns, and, thus, a reluctance to engage with multi-actor business models. Such concerns are not unfounded: In many multi-actor environments, privacy-related misbehavior is common, whether caused by third-party firms, network users, or other actors. Incidents range from unauthorized data sharing to illegal surveillance, identity theft, and financial fraud. Consumers’ skepticism poses challenges for firms, requiring strategic measures that not only foster consumer engagement with the network but also help build trust in the presence of third-party involvement and manage the consequences of trust erosion following negative third-party experiences. While privacy research recognizes consumer concerns in multi-actor environments, the literature does not offer specific business strategies to encourage data disclosure in the context of third-party involvement. Instead, research on multi-actor business models has largely focused on the benefits of shared value creation, while the broader impact of negative third-party privacy-related incidents on the overall actor-network remains underexplored. This dissertation addresses this research gap through three independent essays, aiming to identify business strategies that enhance consumer trust and willingness to disclose data in multi-actor environments and counteract the negative effects of (potential) third-party misbehavior on the actor-network. It focuses on preventive measures that address consumers’ concerns before interacting with the actor-network, as well as reactive strategies that are designed to maintain consumers’ engagement with an actor-network after a negative experience with one of the actors. As a first step toward identifying effective business strategies in multi-actor environments, Essay 1 examines two mechanisms: transparency and control, commonly used to foster consumers’ willingness to disclose data. While not explicitly framed as a multi-actor study, Essay 1 investigates how firms’ implementation of transparency and control features regarding data practices influences consumers’ willingness to disclose data. Two online scenario experiments compare a proactive approach, where consumers receive all relevant information and control options upfront, with an upon-request approach, where they access details by clicking for more information. The results show that the proactive approach increases cognitive effort and reduces data disclosure, while perceptions of procedural fairness do not significantly differ between the approaches. This pattern also holds in high-sensitivity conditions involving third-party data sharing, suggesting that the upon-request approach is effective across both single-actor and multi-actor environments. Building on these findings, Essay 2 analyzes how the representation of actor-networks influences consumers’ willingness to disclose data. Drawing on social psychology research on perceived entitativity, three online scenario experiments show that consumers trust firm networks more when they perceive them as highly entitative - as cohesive and integrated entities - rather than as low entitative, meaning a loose collection of independent firms. This greater trust, in turn, enhances their willingness to disclose personal data. Moreover, the analyses reveal that consumers process information about highly entitative firm networks more fluently and experience lower uncertainty than when engaging with low-entitativity networks. This essay also holds substantial practical significance by identifying concrete design recommendations to enhance perceived entitativity. In Essay 3, a platform is conceptualized as a multi-actor environment that enables peer-to-peer interactions. This essay investigates how consumers’ negative experiences with other users in multi-actor environments affect trust in the platform as a whole. Two studies were conducted in the home-sharing context, where consumers grant deep access to their privacy by allowing others into their homes or by sharing images of their private living spaces, addresses, and payment details. This openness entails inherent risks when interacting with other users and may facilitate misbehavior. The findings reveal a negative bottom-up trust transfer, whereby a negative experience with another user leads to diminished trust in the misbehaving user and, subsequently, in the platform. Although the platform does not directly control user behavior, consumers attribute part of the responsibility for negative incidents to the platform, which in turn reduces loyalty. This effect is stronger for negative outcome-related incidents, which pertain to the core service itself, than for negative process-related incidents, which relate to the service delivery process. To effectively mitigate this erosion of loyalty, Essay 3 identifies high prior relationship satisfaction between the consumer and the platform as a key buffering mechanism in which firms should actively invest. The insights from my dissertation extend privacy research by examining multi-actor environments and analyzing how firms can encourage consumers to disclose personal data, which psychological mechanisms guide their decision-making, and how trust is established and transferred within the network. Furthermore, this dissertation raises corporate awareness of the risks associated with the involvement of third parties in multi-actor settings. It provides practical strategies to mitigate these risks, fostering consumer acceptance and ensuring the long-term success of such business models. N2 - Technologische Fortschritte und die zunehmende Digitalisierung der letzten Jahrzehnte haben dazu geführt, dass immer mehr Unternehmen den Wert von Multi-Akteurs-Geschäftsmodellen erkennen. Die Integration von Drittakteuren in Geschäftsprozesse bietet Unternehmen zahlreiche Vorteile, darunter den Zugang zu zusätzlichen Ressourcen, Kompetenzen und Informationen. Aus Konsumentensicht ist die Einbindung von Drittparteien, die den Konsumenten häufig unbekannt sind, jedoch oftmals mit Unsicherheit und Datenschutzbedenken verbunden und führt folglich zu einer geringeren Bereitschaft, mit Multi-Akteurs-Geschäftsmodellen zu interagieren. Diese Bedenken sind nicht unbegründet: In vielen Multi-Akteurs-Umgebungen ist datenschutzbezogenes Fehlverhalten weit verbreitet, unabhängig davon, ob es von Drittunternehmen, Netzwerknutzern oder anderen Akteuren ausgeht. Die Vorfälle reichen von unautorisierter Datenweitergabe über illegale Überwachung und Identitätsdiebstahl bis hin zu finanziellem Betrug. Die Skepsis der Konsumenten stellt Unternehmen vor erhebliche Herausforderungen und erfordert strategische Maßnahmen, die nicht nur die Interaktion der Konsumenten mit dem Netzwerk fördern, sondern auch Vertrauen trotz der Einbindung von Drittakteuren aufbauen und die Folgen von Vertrauensverlusten nach negativen Erfahrungen mit Drittparteien abfedern. Zwar erkennt die Datenschutzforschung Konsumentenbedenken in Multi-Akteurs-Umgebungen an, die bestehende Literatur bietet jedoch kaum konkrete unternehmerische Strategien zur Förderung der Datenpreisgabe im Kontext von Drittparteien. Stattdessen konzentriert sich die Forschung zu Multi-Akteurs-Geschäftsmodellen bislang überwiegend auf die Vorteile gemeinsamer Wertschöpfung, während die umfassenderen Auswirkungen negativer, datenschutzbezogener Vorfälle durch Drittakteure auf das gesamte Akteursnetzwerk weitgehend unerforscht bleiben. Diese Dissertation adressiert diese Forschungslücke in drei eigenständigen Essays mit dem Ziel, unternehmerische Strategien zu identifizieren, die das Vertrauen der Konsumenten und ihre Bereitschaft zur Datenpreisgabe in Multi-Akteurs-Umgebungen stärken sowie die negativen Effekte (potenziellen) Fehlverhaltens von Drittakteuren auf das Akteursnetzwerk abmildern. Der Fokus liegt dabei sowohl auf präventiven Maßnahmen, die Konsumentenbedenken bereits vor der Interaktion mit dem Akteursnetzwerk adressieren, als auch auf reaktiven Strategien, die darauf abzielen, das Engagement der Konsumenten nach einer negativen Erfahrung mit einem der Akteure aufrechtzuerhalten. Als ersten Schritt zur Identifikation wirksamer Strategien in Multi-Akteurs-Umgebungen untersucht Essay 1 zwei etablierte Mechanismen zur Förderung der Datenpreisgabebereitschaft von Konsumenten: Transparenz und Kontrolle. Obwohl Essay 1 nicht explizit als Multi-Akteurs-Studie konzipiert ist, analysiert er, wie die Umsetzung von Transparenz- und Kontrollmechanismen in Bezug auf Datenpraktiken die Bereitschaft der Konsumenten zur Datenpreisgabe beeinflusst. Zwei Online-Szenarioexperimente vergleichen einen proaktiven Ansatz, bei dem Konsumenten alle relevanten Informationen und Kontrollmöglichkeiten unmittelbar erhalten, mit einem Upon-Request-Ansatz, bei dem weiterführende Informationen erst durch aktives Anklicken zugänglich sind. Die Ergebnisse zeigen, dass der proaktive Ansatz den kognitiven Aufwand erhöht und die Datenpreisgabe reduziert, während sich die Wahrnehmung prozeduraler Fairness zwischen den Ansätzen nicht signifikant unterscheidet. Dieses Muster zeigt sich auch in Hochsensitivitätsbedingungen, die eine Datenweitergabe an Drittparteien beinhalten, was darauf hindeutet, dass der Upon-Request-Ansatz sowohl in Single-Actor- als auch in Multi-Akteurs-Umgebungen effektiv ist. Aufbauend auf diesen Ergebnissen analysiert Essay 2, wie die Darstellung von Akteursnetzwerken die Bereitschaft der Konsumenten zur Datenpreisgabe beeinflusst. In Anlehnung an sozialpsychologische Forschung zur wahrgenommenen Entitativität zeigen drei Online-Szenarioexperimente, dass Konsumenten Netzwerken von Unternehmen stärker vertrauen, wenn sie diese als hoch entitativ – also als kohäsive und integrierte Einheiten – wahrnehmen, im Vergleich zu niedrig entitativen Netzwerken, die als lose Zusammenschlüsse unabhängiger Unternehmen erscheinen. Dieses erhöhte Vertrauen steigert wiederum die Bereitschaft zur Preisgabe persönlicher Daten. Darüber hinaus zeigen die Analysen, dass Konsumenten Informationen über hoch entitative Unternehmensnetzwerke fließend verarbeiten und geringere Unsicherheit empfinden als bei niedrig entitativen Netzwerken. Dieser Essay besitzt zudem eine hohe praktische Relevanz, da er konkrete Gestaltungsempfehlungen zur Erhöhung der wahrgenommenen Entitativität identifiziert. In Essay 3 wird eine Plattform als Multi-Akteurs-Umgebung konzeptualisiert, die Peer-to-Peer-Interaktionen ermöglicht. Der Essay untersucht, wie negative Erfahrungen von Konsumenten mit anderen Nutzern in Multi-Akteurs-Umgebungen das Vertrauen in die Plattform als Ganzes beeinflussen. Zwei Studien wurden im Kontext der privaten Wohnraumvermietung durchgeführt, in dem Konsumenten tiefgehenden Zugang zu ihrer Privatsphäre gewähren, indem sie anderen Personen Zutritt zu ihren Wohnungen erlauben oder Bilder privater Wohnräume, Adressdaten und Zahlungsinformationen teilen. Diese Offenheit ist mit inhärenten Risiken verbunden und kann Fehlverhalten durch andere Nutzer begünstigen. Die Ergebnisse zeigen einen negativen Bottom-up-Vertrauenstransfer: Eine negative Erfahrung mit einem anderen Nutzer führt zu einem Vertrauensverlust gegenüber dem fehlhandelnden Nutzer und in der Folge auch gegenüber der Plattform. Obwohl die Plattform das Verhalten der Nutzer nicht direkt kontrolliert, schreiben Konsumenten ihr dennoch eine Mitschuld an negativen Vorfällen zu, was die Loyalität gegenüber der Plattform verringert. Dieser Effekt ist stärker bei ergebnisbezogenen Vorfällen, die den Kern der Dienstleistung betreffen, als bei prozessbezogenen Vorfällen, die sich auf die Leistungserbringung beziehen. Um dieser Loyalitätserosion wirksam entgegenzuwirken, identifiziert Essay 3 eine hohe vorherige Beziehungszufriedenheit zwischen Konsument und Plattform als zentralen Puffermechanismus, in den Unternehmen gezielt investieren sollten. Die Erkenntnisse dieser Dissertation erweitern die Datenschutzforschung, indem sie Multi-Akteurs-Umgebungen in den Fokus rücken und analysieren, wie Unternehmen Konsumenten zur Preisgabe persönlicher Daten motivieren können, welche psychologischen Mechanismen ihre Entscheidungsprozesse steuern und wie Vertrauen innerhalb von Netzwerken entsteht und übertragen wird. Darüber hinaus schärft die Dissertation das Bewusstsein von Unternehmen für die Risiken, die mit der Einbindung von Drittakteuren in Multi-Akteurs-Konstellationen verbunden sind. Sie liefert praxisnahe Strategien zur Reduktion dieser Risiken, fördert die Akzeptanz solcher Geschäftsmodelle bei Konsumenten und trägt damit zu deren langfristigem Erfolg bei. KW - Privacy Research KW - Marketing KW - Multi-Actor Environment KW - Trust KW - Transparency KW - Verbraucherverhalten KW - Marketing KW - Privatsphäre KW - Vertrauen KW - Transparenz Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-19957 ER - TY - THES A1 - Kochendörfer, Laura T1 - Rethinking systems use in information systems research – theories on individuals’ use of multiple information systems N2 - While individuals use multiple information systems (IS) every day, research on IS use predominantly investigates use with respect to only one information system at a time. In light of this “single-IS paradigm” (Gerlach & Cenfetelli, 2022), theoretical and empirical insights into the nature and behavioral manifestations of multiple IS use remain limited. To advance the discipline’s understanding of the multiple IS use reality, this dissertation theorizes mechanisms that are idiosyncratic to the context of individuals’ multiple IS use. Based on grounded theory methodology and interview data from individuals using multiple IS, this dissertation contributes two theories on multiple IS use in two essays. The first essay introduces an analytical theory of eight different interdependencies-in-use as core mechanisms that emerge as users engage with multiple IS. The second essay builds on these interdependencies-in-use and examines how one type of interdependency manifests in behavior. The resulting process theory explains a behavioral phenomenon resulting from multiple IS use: users transferring usage behaviors from one IS to another. This dissertation contributes a theoretical framework for conceptualizing multiple IS use with its underlying mechanisms that enable future research to systematically investigate multiple IS use and related phenomena. It further enriches insights on usage behavior by a multiple IS perspective, indicating that multiple IS use contexts give rise to unique behavioral dynamics. With that, the current conversation in IS use research that focuses on single IS use is extended with new theoretical insights on the use of multiple IS. The dissertation offers additional recommendations for practitioners to consider the interdependent way individuals use multiple IS. KW - Information systems KW - IS use KW - Interdependence KW - multiple IS use Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-20164 ER - TY - THES A1 - Hosseini, Amir T1 - Integrated Scheduling and Material Handling: Theory and Applications in Manufacturing Systems N2 - Scheduling concerns the allocation of limited resources to competing tasks over time and is central to manufacturing systems. In practice, production scheduling is tightly linked to material handling, as jobs must be transported between machines, buffers, and storage locations. However, transportation decisions have often been simplified or decoupled from classical scheduling models. This dissertation investigates the problem of Integrated Scheduling and Material Handling (ISMH), where processing and transportation decisions are jointly optimized to improve overall system performance. The thesis develops this objective in three steps. First, it provides a structured and unified classification of scheduling problems with transportation elements, organizing a fragmented body of literature and clarifying methodological foundations. Second, it studies scheduling in AGV-based material handling systems under battery constraints, proposing a novel mixed-integer programming formulation and an exact solution approach based on logic-based Benders decomposition. Third, it extends integrated models to a buffer-constrained flow shop setting with mobile buffering, introducing a decomposition-based algorithm that significantly improves scalability. Together, these contributions advance conceptual understanding, modeling frameworks, and exact solution methods for integrated production and internal logistics planning in modern manufacturing systems. KW - Scheduling KW - Material Handling KW - Mixed Integer Programming-MIP KW - Logic Based Benders Decomposition Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-20249 ER - TY - THES A1 - Rolvering, Geske T1 - Empirical Essays in Public Economics N2 - This dissertation exploits quasi-experimental methods and rich microdata to identify causal effects of public interventions that speak directly to the goals outlined in the 2030 Agenda for Sustainable Development. Specifically, the first chapter analyzes the effect of public child care provision on mothers’ career trajectories, focusing on the timing of labor market re-entry and the quality of occupational outcomes. It thereby contributes to the Sustainable Development Goals of "Gender Equality", "Reducing Inequalities", and "Decent Work and Economic Growth". The second chapter investigates the impact of all-day school programs on juvenile property, violent, and drug-related crime. By providing evidence on how school schedules can be structured to promote safe learning environments, it contributes in particular to the goal of "Quality Education". In addition, it also contributes to broader objectives related to "Good Health and Well-Being" as well as "Peace, Justice and Strong Institutions". The third chapter examines public attitudes toward climate change and carbon pricing in Germany and analyzes whether different types of information shift people’s policy views. By exploring measures to reduce resistance to effective but politically unpopular environmental policies, this chapter contributes to the goal of "Climate Action". KW - Public Economics KW - Applied Microeconomics Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-20186 ER - TY - THES A1 - Khosravi, Mohammad T1 - Hard Instances, Improved Algorithms and New Interdiction Models for Robust Optimization N2 - Robust combinatorial optimization seeks solutions that remain effective across all possible realizations of an uncertainty set, making the choice of this set a crucial factor in both the complexity and practical applicability of robust models. A key challenge in this field is striking a balance between computational tractability and solution quality, particularly when dealing with large uncertainty sets. This dissertation advances the field of robust optimization by addressing three central themes: (i) methods for generating hard instances and establishing a benchmark library, (ii) high-quality exact solution methods and approximation algorithms, and (iii) the modeling of uncertainty sets and their impact on problem complexity. The absence of a benchmark library for robust optimization problems makes it difficult to conduct fair and effective comparisons of different solution methods. As a result, researchers often rely on randomly generated instances, which may hinder meaningful evaluations. To address this issue, this work develops optimization-based and heuristic methods for generating challenging instances of robust problems. Additionally, to facilitate more consistent and insightful comparisons of solution algorithms with minimal effort, we introduce a standardized benchmark library for use by the research community. To tackle the computational challenges posed by large uncertainty sets, this dissertation proposes scenario reduction techniques specifically designed for robust optimization. These methods aim to reduce the size of the uncertainty set while preserving the objective value as accurately as possible. Unlike traditional clustering approaches, this formulation treats scenario reduction as an optimization problem independent of the underlying decision-making model, enabling structured reductions with theoretical performance guarantees. Experimental results demonstrate that this approach produces solutions of comparable or superior quality compared to those obtained through general-purpose clustering techniques. Building on this framework, we further refine scenario reduction by incorporating information about the structure of feasible solutions. While previous reduction methods focused exclusively on the uncertainty set, we show that integrating knowledge of feasible solutions leads to improved uncertainty sets and more accurate robust models. Through a combination of theoretical analysis and computational experiments, we establish the effectiveness of this approach in enhancing both tractability and solution quality in robust combinatorial optimization. Finally, we introduce a novel variant of discrete budgeted uncertainty for cardinality-based constraints or objectives, incorporating a weight vector into the budget constraint. Our theoretical analysis reveals that while the adversarial problem can be solved in linear time, the robust problem becomes NP-hard and non-approximable. Nonetheless, we propose and evaluate alternative modeling approaches that demonstrate promising scalability in practice. This dissertation contributes to robust optimization by offering new perspectives on uncertainty modeling, algorithmic techniques for scenario reduction, and complexity analyses of key robust problems. The proposed methods provide both theoretical guarantees and practical advancements, paving the way for more efficient and scalable robust optimization models. Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-20399 ER - TY - THES A1 - Wirth, Olivia T1 - Digital Platforms and Mobile Technologies for Development: Essays on Firm Formalization, Financial Inclusion, and Agricultural Resilience in Sub-Saharan Africa N2 - Mobile technologies and digital platforms have expanded rapidly across sub-Saharan Africa, creating opportunities to strengthen state capacity, broaden financial inclusion, and build agricultural resilience. This dissertation examines their development impacts across three settings. Chapter 1 combines novel administrative tax records with high resolution data on mobile network rollout in Uganda to estimate the effects of mobile internet access on firm tax behavior and public revenue. Exploiting plausibly exogenous rollout timing, we find that improved access increases firm formalization and expands the tax base, strengthening revenue collection. Chapter 2 presents a randomized controlled trial in Niger—the world’s most financially excluded country—to identify barriers to adoption of a mobile money platform. Information provision raises awareness but not use, consistent with information being necessary but insufficient for diffusion. By contrast, a modest financial incentive significantly increases both adoption and usage. Chapter 3 uses a household panel collected before and after a severe drought in northern Ghana and exploits variation in rainfall in a differences-in-differences framework to estimate effects on production, income sources and adaptation plans. We find that increasing drought severity lowers soybean yields and revenues and decreases reliance on own-business income and remittances, reflecting broader livelihood impacts. Farmers who use mobile phones to access agricultural information make different input choices and adaptation plans. Taken together, these findings highlight the promise of mobile technologies and digital platforms for development. Their effectiveness, however, depends on complementary infrastructure and local capacity, underscoring the need for scalable, context-specific strategies to harness these tools for inclusive growth and resilience. KW - Digitalisierung KW - Mobilität KW - Landwirtschaft KW - Afrika Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-20049 ER -