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on Innovation |
| By: | Andres Rodriguez-Pose; Zhuoying You; Peter Teirlinck |
| Abstract: | This This paper explores the relationship between support for extreme political parties and research and innovation across regions in the European Union (EU). Extreme parties often exhibit deep scepticism towards expertise and science, with extreme right-wing parties, in particular, challenging the legitimacy of climate change; an attitude that may weaken green research and innovation. We draw on data from 1, 137 EU regions —including scientific publication and patent records— and apply Tobit regression models to find that stronger support for extreme parties is associated with lower levels of scientific research and technological innovation, both overall and in their green forms. While this pattern is visible across the political spectrum, important differences emerge. Support for extreme right-wing parties is consistently tied to reduced research output and innovation performance, particularly in green technological sectors. By contrast, the relationship with extreme left-wing support is more variable, depending on the degree of radicalism, and shows no consistent negative connection with green innovation. |
| Keywords: | research, innovation, climate change, extreme parties, regions, Europe |
| JEL: | D72 D74 O32 O33 R10 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2525 |
| By: | Frederic-Alexander Starmann (Paderborn University); Sylvia Hubner-Benz Author-2-Name-First: Sylvia Author-2-Name-Last: Hubner-Benz (Paderborn University); Michael Frese Author-3-Name-First: Michael Author-3-Name-Last: Frese (Asia School of Business; Leuphana University of Lueneburg); Zhaoli Song Author-4-Name-First: Zhaoli Author-4-Name-Last: Song (National University of Singapore) |
| Abstract: | While the importance of teams for innovation and entrepreneurship is well acknowledged, research shows that teams often struggle to generate or select ideas effectively. However, research suggests that teams’ strengths show in idea elaboration, that is, in collectively developing ideas further. Yet despite its apparent relevance, this crucial phase in the idea journey remains understudied, particularly the interactions that drive successful elaboration in exploratory innovation. This study examines how entrepreneurial teams elaborate ideas through their interactions and how different modes of collective idea elaboration shape the creative output. Through qualitative video analysis of 79 entrepreneurial teams during a 60-minute exploratory innovation task, we identified four distinct modes of collective idea elaboration. Creative synthesis transforms ideas through collective reasoning, while patchworking aggregates ideas through parallel individual reasoning. Static evaluation involves judgment without development, and (expedited) closure reflects minimal collective engagement with both team and content. We characterize these modes along two analytical dimensions, engagement intensity (deep versus superficial) and engagement orientation (developmental versus evaluative), and develop a theoretical model highlighting their dynamic and situated nature. By taking a dialogical perspective, we show when and why these dimensions of engagement shift throughout the creative work process and shape an idea’s trajectory. Our findings contribute to the literature on team creativity and innovation. (abstract of the paper) |
| Keywords: | Team Creativity and Innovation, Dialogical Perspectives, Process, Idea Elaboration, Exploratory Innovation |
| JEL: | L26 O31 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:dispap:154 |
| By: | Burr, Wolfgang |
| Abstract: | This paper examines the changes in the innovation ecosystem of the German automotive industry for the technology field of automotive software from the perspective of individual firms. The change of the innovation ecosystem of the German car industry through introduction of software-defined vehicles is being classified as a radical transformation. Focus of the analysis is the beginning formation of the software ecosystem in the German car industry. Based on qualitative data, three explorative company case studies on three leading German car manufacturers Volkswagen, BMW and Mercedes-Benz are presented. The exploratory case studies focus on company-centric innovation ecosystems of VW, BMW and Mercedes in the software sector which are integrated into an overall software innovation system of the German automotive industry. The case studies aim is to analyse the formation phase of the digital innovation ecosystem of German car manufacturers. It intends to contribute towards a further development of the innovation ecosystem concept and a better understanding of the formation phase of an innovation ecosystem. |
| Keywords: | Innovation Ecosystems, Digital Innovation, Software, Automotive Industry, Germany |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:stuist:323954 |
| By: | Christian William Callaghan |
| Abstract: | This paper develops a theoretical and formal response to the collapse in the marginal cost of ideation caused by artificial intelligence (AI). In challenging the foundational assumption of knowledge scarcity, the paper argues that the key economic constraint is no longer the generation of ideas, but the alignment of ideation with the recursive structure of human needs. Building on previous work, we further develop Experiential Matrix Theory (EMT), a framework that models innovation as a recursive optimisation process in which alignment, rather than ideation, becomes the binding constraint. Accordingly, we formalise core mechanisms of EMT and apply it to the dynamics of ideation collapse and institutional realignment under AI. Using a series of defensible economic models, we show that in this post-scarcity paradigm, the creation of economic and social value increasingly accrues to roles that guide, interpret, and socially embed ideation, rather than to those that merely generate new ideas. The paper theorises a transition from a knowledge economy to an alignment economy, and derives policy implications for labor hierarchies, subsidy structures, and institutional design. The university, in this context, must invert its function from knowledge transmission to epistemic alignment. The paper concludes by reframing growth not as a function of knowledge accumulation, but of how well society aligns its expanding cognitive capacity with the frontier of experiential human value. This redefinition of the innovation constraint implies a transformation of growth theory, policy design, and institutional purpose in the AI era. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.07019 |
| By: | Gamal Atallah; Aggey Simons (Department of Economics, University of Ottawa, Canada) |
| Abstract: | We analyze innovation incentives under price cap regulation by examining scenarios with endogenous price caps, both with and without regulatory commitment. In a setting without informational imperfections, our analysis reveals two principal conclusions. First, there is no trade-off between static and dynamic efficiency. Strengthening firm incentives by allowing it to charge higher prices, and thus realize greater profits, leads to less innovation because it reduces output. The optimal strategy to boost innovation and maximize welfare is to set a low price (and thus, a low profit) target, as innovation incentives are proportional to output. Second, the benefits of regulatory commitment for innovation and welfare are not unambiguously clear: commitment neither consistently outperforms nor underperforms non-commitment. Under demand uncertainty, when the firm is risk-averse, the static-dynamic efficiency trade-off reappears, and the firm may prefer non-commitment due to risk-shielding. Under asymmetric information about firm demand type, the trade-off between static and dynamic efficiency becomes inherent (due to information rents and contract distortions), and commitment becomes unambiguously crucial for fostering innovation by preventing the ratchet effect. |
| Keywords: | Price cap regulation; Regulation, Innovation, R&D, Dynamic efficiency; Commitment. |
| JEL: | D42 L12 L43 L51 O31 O38 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ott:wpaper:2504e |
| By: | Mohammad Hossein Azin; Hessam Zandhessami |
| Abstract: | This paper introduces a novel visual mapping methodology for assessing strategic alignment in national artificial intelligence policies. The proliferation of AI strategies across countries has created an urgent need for analytical frameworks that can evaluate policy coherence between strategic objectives, foresight methods, and implementation instruments. Drawing on data from the OECD AI Policy Observatory, we analyze 15-20 national AI strategies using a combination of matrix-based visualization and network analysis to identify patterns of alignment and misalignment. Our findings reveal distinct alignment archetypes across governance models, with notable variations in how countries integrate foresight methodologies with implementation planning. High-coherence strategies demonstrate strong interconnections between economic competitiveness objectives and robust innovation funding instruments, while common vulnerabilities include misalignment between ethical AI objectives and corresponding regulatory frameworks. The proposed visual mapping approach offers both methodological contributions to policy analysis and practical insights for enhancing strategic coherence in AI governance. This research addresses significant gaps in policy evaluation methodology and provides actionable guidance for policymakers seeking to strengthen alignment in technological governance frameworks. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.05400 |
| By: | Alexander Pitharides |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:sus:susphd:0124 |