Regulatory uncertainty is a central channel through which policy affects financial decisions. We develop a programmatic conceptual framework for studying regulatory uncertainty in asset allocation and asset pricing. We define regulatory uncertainty as uncertainty about the design, timing, implementation, interpretation, and enforcement of regulations that directly shape cash flows, feasible actions, institutional constraints, and market expectations. This perspective distinguishes regulatory uncertainty from broader economic policy uncertainty by focusing on regulations, regulatory parameters, and enforcement practices rather than on policy uncertainty in general. The framework identifies four main entry points into financial models: cash-flow dynamics, beliefs and learning, regulatory constraints including tax constraints, and intermediary capital and liquidity constraints. It also clarifies when regulatory uncertainty is merely reflected in expected cash flows and when it commands a risk premium. We illustrate the framework in selected domains in which regulatory uncertainty is economically important: financial regulation, climate policy, taxation, and insurance. We thereby provide a common language for connecting theoretical modeling, measurement, and empirical analysis of regulatory uncertainty and for deriving implications for investors, firms, intermediaries, and regulators.
Research in the field of service ecosystems (SE) in the business-to-business (B2B) context is still in its infancy, especially lacking consideration of social dynamics that shape collaboration among firms. This study focuses on this topic and investigates how companies interact, coordinate, and co-create value within in an industrial SE. Applying structuration theory as the theoretical foundation for analyzing the dynamics and relationships between actors in an industrial SE, we conduct a single case study focusing on the ecosystem revolving around an Industrial Internet of Things software startup as the focal firm, its business partners, and customers. The investigated ecosystem consisted of 17 firms, with 29 interviews building the foundation for analysis. We generate insights on the underlying mechanisms that enable cooperation, knowledge exchange, and joint value creation. Based on our findings, we derive six interrelated modalities that explain how firms align interests, distribute responsibilities, and build shared understanding within the ecosystem. We synthesize these findings into three core dynamics that are integrated into a recursive loop that builds a self-containing system providing both structure and flexibility for change. By providing empirical insights into these dynamics, the study enhances our understanding of how SEs function as complex socio-technical systems. The study contributes to both research and practice by explaining how firms can strengthen collaboration and manage interdependencies to achieve collective value creation.
IT projects have, compared to other domains, higher failure rates. This phenomenon is even more pronounced in the Data Analytics (D A) context. We notice that shortcomings in existing D A development and operation frameworks are one major reason for D A project delay, misorientation and failure and that mitigation of the shortcomings promises improvements for development and operations practices. We propose the D A stage gate process as a guiding framework to manage D A use cases along six stages: ideate, pre-assess, assess, prototype, develop, and operate. We define the sequence of actions required to develop D A use cases efficiently, value-centered and comprehensively. Our results show that especially prior to use case development, ideation and assessment remain underrepresented, portfolio aspects are missed and no solid go- and kill criteria are operationalized. We further acknowledge that D A use cases come in great diversity and that they are embedded in a highly dynamic environment complicating their value-effective management. To address this, we assess these differentiating and dynamic factors of D A use cases and evaluate their influence on management practices. We designed the stage gate process in collaboration with a panel of 17 industry experts across three iterative cycles and assessed our findings through an ex-post evaluation with three industry cases. The resulting design principles and best practices offer practitioners structured points of intervention to close the persistent gap between D A technological potential and actual business value realization.
This paper investigates the timetabling problem in project-oriented schools and is based on the real-world setting of the Universitätsschule Dresden, an experimental school organized around interdisciplinary project work. In project-oriented schools, traditional subject-based instruction is replaced by flexible learning structures that enable individualized learning pathways. However, individual schedules and short project cycles increase the need for efficient planning tools to coordinate resources effectively. Therefore, a mixed-integer linear programming model is proposed to generate such individualized timetables that account for student preferences, the number of opened project sections, room resources, and supervision requirements. These objectives are combined using a weighted-sum approach, enabling decision-makers to balance pedagogical and operational considerations. To address computational scalability, four solution approaches with different degrees of decomposition are examined: a fully integrated model, an optimized post-enrollment strategy, a sequential decomposition, and a decomposition approach enhanced by an improvement step based on partial variable fixing and re-optimization. Computational experiments show that the integrated model becomes intractable for larger instances, while especially the decomposition approach with the improvement step offers a favorable balance between solution quality and computation time. The proposed solution approaches provide insights that may support decision-making in schools with comparable project-based structures.
Existing scholarship has largely prioritized the static structural features of innovation networks, while the dynamic evolution of inter-organizational dependence in such networks has been largely neglected. Addressing this gap, this study employs negative binomial regression analysis on the panel data (spanning 2010 to 2018) of collaborative patents from Chinese listed firms. The aim was to investigate how inter-organizational dependence shapes corporate innovation performance contingent on an increase in cooperation duration and the number of partners (i.e., innovation network scale). The results indicate that longer cooperation duration strengthens relational dependence and enhances innovation performance within stable network boundaries. An expanded network scale weakens inter-organizational knowledge dependence and reinforces structural dependence, collectively facilitating innovation improvements. This study empirically verifies the dynamic mechanism of time-scale dependence shaping the performance of innovation networks. The findings provide insightful theoretical implications and practical guidelines for cultivating resilient corporate innovation ecosystems. Departing from the static paradigm prevailing in innovation network research, this study unveils the nonlinear evolutionary patterns of multi-dimensional inter-organizational dependence along the temporal and network-scale dimensions. It offers an advanced theoretical lens for interpreting the life-cycle dynamics of corporate innovation networks.
