Fraunhofer Institute for Systems and Innovation Research

Abstract Electrifying heavy-duty truck fleets is critical for decarbonization, yet several cutting-edge approaches are based on historical diesel plans which do not account for electric truck characteristics. Such one-to-one replacement of diesel trucks without adapting operational plans may thus underestimate the technical and economic feasibility of battery electric trucks. To better understand the potential for fleet-level optimization, we compare a one-to-one replacement strategy against a holistic re-optimization approach that jointly solves for fleet composition, shipment-to-vehicle allocation, vehicle routing, and charge scheduling. Using real-world data from German grocery logistics (~38,000 shipments), we find that re-optimization unlocks significantly higher electrification potential and economic performance. It increases the electrifiable payload from 48% to 85% and doubles the fleet-level cost reduction (7.0% vs. 3.5%) compared to the optimized diesel baseline. These results demonstrate that maximizing electric fleet viability requires moving beyond simple hardware substitution to fundamentally restructuring operational logistics.

Abstract This study explores the implications of technology availability constraints on the decarbonisation pathways of the EU power sector, drawing on scenario results from five European energy supply models: MEESA, LIMES, ENERTILE, ACSG, and OSeMBE. The analysis evaluates how limiting the deployment of key low-carbon generation technologies, namely carbon capture and storage (CCS), nuclear power, bioenergy, solar photovoltaics, and wind energy, affects the electricity generation mix, emissions reduction, investment needs, and power system costs by 2030 and 2050. Results within the model ensemble indicate that Variable Renewable Energy sources (VRE), wind and solar, are indispensable for deep decarbonisation. Constraints on solar or wind power substantially increase electricity generation costs and require major shifts in technology portfolios, often resulting in greater reliance on remaining renewable options or fossil fuel generation with CCS. The unavailability of CCS leads to higher system emissions and increased investment in renewables and storage. In contrast, removing nuclear or bioenergy has a more moderate impact, though some regional effects are significant. All models show that achieving ambitious emission reductions in the power sector remains technically feasible under individual technology constraints, but the mitigation effort shifts across generation technologies, and system costs rise considerably in low VRE futures. Policy implications include the need for robust support for wind and solar deployment, cross-border system integration, flexible technologies, and backup capacity. The findings underscore the value of a diversified technology portfolio, strategic infrastructure investments, and EU-level coordination to preserve cost efficiency and ensure stable power system performance under uncertainty in future technology availability.

ABSTRACT Small and medium‐sized enterprises (SMEs) often face unique challenges in integrating advanced digital technologies due to limited resources and organizational constraints. While much attention has been given to large firms' adoption of big data analytics (BDA) and artificial intelligence (AI), less is known about how SMEs can leverage these tools effectively. This study addresses the research question of how SMEs, especially SaaS B2B, can transform marketing processes through advanced data analytics, large language models (LLMs), and generative AI. Drawing on a qualitative case study of a SaaS company, this research proposes a framework for integrating web mining and LLMs into a semantic analysis process to improve client acquisition by better targeting potential clients, i.e., leads. It optimizes lead qualification and facilitates the replication of successful cases by automating data collection, generating tailored marketing content, and creating customized commercial proposals. Based on the business analytics success model (BASM) framework, our study investigates the organizational changes necessary to embed innovative data‐driven business processes into existing workflows through process mapping. Key stakeholders evaluate the proposal via interviews, which discuss the barriers to aligning and opportunities associated with adopting AI‐driven solutions and emphasize the importance of technological capabilities with business goals. The findings reveal that for SMEs, adopting advanced analytics is contingent upon overcoming resource constraints through targeted organizational adjustments, such as incorporating dominant logic identification into strategic planning. This research contributes theoretically by customizing the BASM for SaaS SMEs and offering a roadmap for implementing AI‐driven marketing solutions, demonstrating how SMEs can enhance operational efficiency and achieve competitive advantages, even in resource‐constrained environments.

Abstract Innovation indicators show a strong increase in capabilities of latecomer countries in green technologies over the last 20 years. Latecomers have become leading origin of green technology publications, hold 27 per cent of green technology patents and 38 per cent of green technology exports. There is considerable heterogeneity between latecomer countries, indicating that each country follows a specific path that reflects domestic strengths and weaknesses. A review of existing case studies for wind energy, photovoltaics, hydrogen technologies, and (environmentally-friendly) mining technologies shows a set of driving factors for successful latecomer development emerges from typical case studies. We suggest a taxonomy for success factors for green technology latecomer development which consists of characteristics of the specific technology, active demand side and industrial policies of the state, openness to global markets, and exploitation of windows of opportunities with regard to geographical factors.

How to estimate the economic returns of public science is a longstanding but equally challenging topic in quantitative science studies. In this paper, we exploit the staggered foundation of the SENAI Innovation Institutes (ISI) in Brazil since 2012 to estimate their effects on GDP using a difference-in-differences (DiD) approach. Building on historical and institutional insights from interviews on the foundation process, we unravel the conditions under which the parallel trends assumption is likely to hold. Our analysis reveals that these institutes significantly contribute to GDP per capita, with an average treatment effect of 985 BRL (approximately €160). Moreover, by relying on detailed project-level data, we were able to show that the effects come almost exclusively from genuine research projects and not from the provision of scientific services, such as metrology. Finally, tentative calculations suggest that the SENAI ISI institutes may account for about 0.66% of Brazil’s overall GDP, emphasising the importance of applied science in regional economic development and providing insights into effective collaboration between research and industry. • The paper addresses the economic returns of public science, focusing on the SENAI Innovation Institutes (ISI) in Brazil. • Utilizes a difference-in-differences (DiD) approach to estimate the institutes' impact on GDP since their establishment in 2012. • Highlights the importance of historical and institutional insights in validating the parallel trends assumption. • Findings indicate a significant contribution to GDP per capita, with an average treatment effect of 985 BRL (around €160). • The positive effects are primarily attributed to genuine research projects rather than scientific service provision. • Following macroeconomic projections, SENAI ISI institutes represent approximately 0.66% of Brazil’s overall GDP, underscoring the role of applied science in regional economic development.

Abstract Allocation decisions are vulnerable to political influence, but it is unclear in which situations politicians use their discretionary power in a partisan manner. We analyze the allocation of presidential disaster declarations in the United States, exploiting the spatiotemporal randomness of all hurricane strikes from 1965 to 2018 along with changes in political alignment. We show that decisions are unbiased when disasters are either very strong or weak. Only after medium-intensity hurricanes do areas governed by presidents’ co-partisans receive up to twice as many declarations. This hump-shaped political bias explains 8.3% of overall relief spending, totaling about USD 400 million per year.

Predicting career trajectories is a complex yet impactful task, offering significant benefits for personalized career counseling, recruitment optimization, and workforce planning. However, effective career path prediction (CPP) modeling faces challenges including highly variable career trajectories, free-text resume data, and limited publicly available benchmark datasets. In this study, we present a comprehensive comparative evaluation of CPP models—linear projection, multilayer perceptron (MLP), LSTM, and large language models (LLMs)—across multiple input settings and two recently introduced public datasets. Our contributions are threefold: (1) we propose novel model variants, including an MLP extension and a standardized LLM approach, (2) we systematically evaluate model performance across input types (titles only vs. title+description, standardized vs. free-text), and (3) we investigate the role of synthetic data and fine-tuning strategies in addressing data scarcity and improving model generalization. Additionally, we provide a detailed qualitative analysis of prediction behaviors across industries, career lengths, and transitions. Our findings establish new baselines, reveal the trade-offs of different modeling strategies, and offer practical insights for deploying CPP systems in real-world settings.

ABSTRACT Universities are increasingly expected to actively contribute to societal developments that go beyond teaching and research activities. This societal impact is largely based on knowledge transfer and interlinkages between universities and society. However, the patterns and scope of knowledge transfer can vary between universities, implying different transfer profiles with individual settings of transfer mechanisms, transfer partners and their geographical localisation. In this paper we analyse different forms of knowledge transfer profiles in the external communication of German universities, based on an analysis of 145 mission statements. The results show a high diversity of knowledge transfer profiles, both in the intensity and thematic focus of knowledge transfer. Some universities focus more on knowledge transfer to the economy while other universities have a stronger transfer focus on state actors. Regarding the type of higher education institutes, we further find that universities of applied sciences have a higher transfer intensity than universities. However, most universities have a profile with a low overall intensity of knowledge transfer, indicating that knowledge transfer plays a subordinate role in universities’ profiling, despite being a codified task.

Pay-As-You-Throw (PAYT) tariff schemes, in which households pay based on their waste generation, are proposed as solutions to the growing worldwide challenge of municipal solid waste management. However, public acceptance of such schemes remains low. Using a one-factor between-subject experimental survey design with 620 participants, we test the effects of environmental and accountability appeals and of individual characteristics in shaping preferences for a proposed PAYT scheme in Grenoble, France. We find a positive effect of the accountability appeal and no effect of the environmental appeal on preference for the PAYT scheme compared to a fixed-fee scheme. Additional analyses suggest that accountability appeals are particularly effective for individuals with below-median age, above-median income, and at least a master's degree, indicating that policymakers should target younger and educated citizens with these appeals in PAYT campaigns. Future research could test the applicability of these findings in other settings and for other waste-related interventions.