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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.
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.
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".
Integrated Scheduling and Material Handling: Theory and Applications in Manufacturing Systems
(2026)
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.
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.
Stranger Danger? Three Essays on Consumer Trust and Data Disclosure in Multi-Actor Environments
(2026)
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.
Tech titans and crypto giants : mutual returns predictability and trading strategy implications
(2024)
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.
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.
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.
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).