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Small-world networks, dynamics and proximity in investment decisions
No full textUsing deal-level micro data from the Dealroom database, we construct a dynamic co-investment syndication network to examine the influence of cultural proximity and geospatial proximity between investors and start-ups, as well as the network position of global VC firms on investment decisions in European-based start-ups. By applying a linear probability regression model with highdimensional fixed effects over the period 2015-2022, we confirm that both cultural and spatial proximity significantly facilitate VC investment. Moreover, our analysis reveals that a prominent network position - characterized by how well-connected (degree centrality) and how influential (Katz centrality) within the co-investment network- substantially enhances VC investments on account of the facilitated sharing of information, contacts, and resources among investors. Furthermore, our findings reveal that small-world networks, characterized by high clustering coefficients, facilitate investments in distant start-ups, helping to overcome spatial constraints-an aspect largely overlooked in the literature. Small-world syndication networks foster trust among members, complementing each other through differentiation and specialization in industrial knowledge and local markets, potentially altering risk-averse behaviour and enabling investments that transcend geographical boundaries
Adaptation Decisions under Climate Change Uncertainty and Weather Extremes
No full textFarms and landowners seem reluctant to invest in climate change adaptation despite socio-economic benefits. However, a dynamic economic model suggests that under uncertain future climate change, observed adaptation “reluctance” may be in fact optimal from the decision-makers’ perspective. This is because decision-makers consider the value of waiting to gather information about future climate and weather before committing to investment. To investi-gate how future climate change shapes adaptation behavior, this study presents an analytical framework that integrate scenario-based climate model projections into a dynamic economic model of the adaptation decision. Using adaptation decision of the Austrian farms as a case example, we use projections of temperature and precipitation under the Shared Socioeconomic Pathways (SSP) scenarios from Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The future projections are used to calibrate the climate change parameters within the economic model to analyze adaptation responses. We find that adaptation reluctance in-tensifies particularly in response to revenue downside shocks induced by weather extremes, and due to increased uncertainty in future climate change the likelihood of adaptation under severe climate change scenarios is not necessarily higher than under mild scenarios. We also find that adaptation subsidies can enhance the likelihood. These results suggest that with in-creasing frequency and intensity of extreme events in future, the expected loss without adap-tation will increase while the reluctance will also increase, and the adaptation investment tim-ing will appear even more delayed. Such delays may incentivize policymakers to provide public interventions such as subsidies to induce adaptation investment. However, they should carefully compare the value of adaptation reluctance and the social costs of the reluctance (e.g., negative externalities) to justify the interventions.Kodama, Friederichs, Szemkus, Seifert and Hüttel gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – through CRC (SFB) 1502/1–2022 – project id: 45005826
Unveiling the masking effect: The role of R&D human capital in collaborative innovation and sustainability
No full textPurpose - This research aims to investigate the causal relationships among collaborative innovation, R&D human capital, and sustainable innovation. Emphasize the mediating role of R&D human capital in the link between collaborative innovation and sustainable innovation. Design/methodology/approach - Our research utilizes data from Chinese A-share listed companies spanning 2009 to 2022. We first identify a solid causal relationship between collaborative innovation and sustainable innovation. Then, the existence of a masking effect role of R&D human capital between the two is revealed. Grouped and quantile regression analyses are further employed to explore the heterogeneities of the masking effect across different cooperation types, asset scales, and industries. Findings - Our findings demonstrate that collaborative innovation significantly enhances sustainable innovation through R&D human capital, but the masking effect of R&D human capital slightly dampens this enhancement. The enhancement is evident in inter-firm collaborations but not in industry-university partnerships. It holds across enterprise sizes, but the effect is more pronounced for firms with lower R&D levels. We argue for the importance of prioritizing quality over quantity in collaborative and sustainability. Practical implications - Firms can benefit from human resources (HR) sharing. Policymakers should back human resource integration to boost innovation quality and efficiency. Originality/value - We are the first to study the mediating role of R&D human capital in connecting collaborative and sustainable innovation. While previous studies on the effects of collaboration have mainly focused on the quantity of innovation outcomes, we find that an excessive emphasis on quantity can negatively affect sustainability
AI, innovation and the public good: A new policy playbook
No full textWhen Chinese start-up DeepSeek released R1 in January 2025, the groundbreaking open-source artificial intelligence (AI) model rocked the tech industry as a more cost-effective alternative to models running on more advanced chips. The launch coincided with industrial policy gaining popularity as a strategic tool for governments aiming to build AI capacity and competitiveness. Once dismissed under neoliberal economic frameworks, industrial policy is making a strong comeback with more governments worldwide embracing it to build digital public infrastructure and foster local AI ecosystems. This paper examines how the national innovation system framework can guide AI industrial policy to foster innovation and reduce reliance on dominant tech companies
Game theory framework for mitigating the cost pendulum in public construction projects
No full textThe coexistence of the winner's curse and cost overruns in the construction industry implies a cost pendulum in which the winning bid is undervalued, whereas the final payment to the contractor is overvalued. We posit that this results from a strategic interaction between three stakeholders: the public agency (PA), the project manager (PM), and the winning contractor, and we propose a game-theoretic framework to model this dynamic. In the current state of practice, the subgame between the contractor and the PM leads to opportunistic contractor behavior and lenient supervision, resulting in increased costs for the PA. We analyze how procedural and cultural interventions by the PA, specifically shifting from a low-bid to an average-bid auction and incentivizing stricter PM oversight, alter the strategic equilibrium. Our findings indicate that while each change alone provides limited improvement, implementing both significantly reduces cost overruns by aligning stakeholder incentives. The findings of this analysis provide insight into how public agencies can mitigate the widespread problem of cost overruns
Sparse spanning portfolios and under-diversification with second-order stochastic dominance
No full textWe develop and implement methods for determining whether relaxing sparsity constraints on portfolios improves the investment opportunity set for risk-averse investors. We formulate a new estimation procedure for sparse second-order stochastic spanning based on a greedy algorithm and Linear Programming. We show the optimal recovery of the sparse solution asymptotically whether spanning holds or not. From large equity datasets, we estimate the expected utility loss due to possible under-diversification, and find that there is no benefit from expanding a sparse opportunity set beyond 45 assets. The optimal sparse portfolio invests in 10 industry sectors and cuts tail risk when compared to a sparse mean-variance portfolio. On a rolling-window basis, the number of assets shrinks to 25 assets in crisis periods, while standard factor models cannot explain the performance of the sparse portfolios
The role of financial investors in successful family‐firm takeovers: A configurational approach
No full textFamily firms increasingly opt for an external succession route and sell shares to financial investors. Yet, not all family‐firm takeovers by financial investors are financially successful. To date, however, we lack a nuanced understanding of the conditions under which financial investors' family‐firm takeovers will succeed financially. Our fsQCA study builds on 52 interviews to reveal the interplay of three typical levers that financial investors use (i.e., operational, strategic, and governance measures), the market situation, and investor type. We identify three distinct roles (i.e., incentivizers, optimizers, and adjacent investors) that financial investors take in successful family‐firm takeover cases. We situate our findings in the literature on resources and their orchestration to explain how investors create value in each of the identified paths, and we contribute to the literature on family‐firm succession and the interplay of family firms and financial investors
Deep reinforcement learning approach for real-time airport gate assignment
No full textAssigning aircraft to gates is one of the most important daily decision problems that airport professionals face. The solution to this problem has raised a significant effort, with many researchers tackling many different variants of this problem. However, most existing studies on gate assignment contain only a static perspective without considering possible future disruptions and uncertainties. We bridge this gap by looking at gate assignments as a dynamic decision-making process. This paper presents the Real-time Gate Assignment Problem Solution (REGAPS) algorithm, an innovative method adept at resolving pre-assignment issues and dynamically optimizing gate assignments in real-time at airports through the integration of Deep Reinforcement Learning (DRL). This work represents the first time that DRL is used with real airport data and a configuration containing a large number of flights and gates. The methodology combines a tailored Markov Decision Process (MDP) formulation with the Asynchronous Advantage Actor-Critic (A3C) architecture. Multiple factors, such as flight schedules, gate availability, and passenger walking time, are considered. An empirical case study demonstrates that the REGAPS outperforms two classic deep Q-learning algorithms and a traditional Genetic Algorithm in terms of reducing passenger walking time and apron gate assignment. Finally, supplementary experiments highlight REGAPS's adaptability under various gate assignment rules for international and domestic flights. The finding demonstrates that not only did REGAPS outperform COVID restrictions, but it can also produce considerable benefits under other policies
The impact of COVID-19 on global stock markets: Comparative insights from developed, developing, and regionally integrated markets
No full textThis study examines the impact of the COVID-19 pandemic on global stock markets by comparing developed and developing economies, while highlighting regional differences. Using dynamic panel regression models, this study explores the role of pandemic-related variables, fiscal policies, and investor sentiment in shaping market performance. Developed markets, although highly sensitive to infections, benefited from robust fiscal interventions and institutional resilience. Developing markets face greater volatility owing to stringent measures, structural vulnerabilities, and limited fiscal capacities. Regionally, Europe demonstrated resilience through coordinated policies, whereas the Americas experienced significant volatility from fragmented responses. Africa and parts of Asia encountered fewer initial shocks but struggled with prolonged recovery due to limited financial and institutional resources. The findings underscore the importance of economic integration, coordinated fiscal and monetary policies, and investor sentiment management to stabilize markets during crises. These insights guide policymakers in enhancing resilience and fostering sustainable economic growth amid future global disruptions