|
on Innovation |
| By: | Ali, Amjad; Afzal, Muhammad Bilal; Ahmad, Khalil |
| Abstract: | This study investigates how market concentration, specifically, the degree of competition within a sector impacts different innovation strategies, with particular emphasis on the distinction between long-term and short-term innovation approaches adopted by corporations. The research utilizes a dataset comprising an unbalanced panel of U.S based firms. To generate robust and valid conclusions, the analysis incorporates a suite of statistical and econometric methodologies, such as regression analysis, multicollinearity diagnostics, tests for endogeneity, and comprehensive robustness assessments. These tools are employed to examine the connection between market concentration, measured by the Herfindahl-Hirschman Index, and the innovation horizon, defined as the interval between initial research and development investments and the attainment of innovative outcomes. Furthermore, the robustness analyses confirm the reliability of the findings across various modeling specifications, providing empirical evidence that heightened market concentration correlates significantly with a reduced innovation horizon. The results reveal that firms operating in markets characterized by high concentration are inclined toward short-term innovation strategies, likely as a result of intense competitive dynamics among a limited number of dominant players striving to retain market share. These insights advance the understanding of how market structure shapes the strategic timing of innovation within firms, yielding important implications for innovation policy as well as managerial decision-making. |
| Keywords: | Market Competition, Innovation Horizon, Firm Innovation, Herfindahl-Hirschman Index |
| JEL: | M13 O3 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127526 |
| By: | Smit, Jorrit |
| Abstract: | Increasingly, funding programs in Europe seek to align scientific research agendas with societal challenges. A promissory regime results around emerging science and technology, in which researchers seek to present their work in line with the expectation of disruptive but feasible technological innovation. In the fields of energy, materials and chemistry research, many turn to (prospective) techno-economic assessments as to respond to this demand. Based on fieldwork and an interview study, I show how these quantitative anticipations can function as ‘feasibility filters’ in the process of agenda-setting that implicitly translate the interests, infrastructures and imaginaries of large-scale industries into technical targets for laboratory science. Non-economic considerations come second, after the rate-determining step of the feasibility filter. Finally, I argue that the resulting, paradoxical dynamics of techno-economic alignment is at odds with calls in STS to open up sustainable transformation and innovation pathways via socio-political and economic alternatives. |
| Date: | 2026–01–16 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:eqbaw_v1 |
| By: | Iñaki Aldasoro; Leonardo Gambacorta; Rozalia Pal; Debora Revoltella; Christoph Weiss; Marcin Wolski |
| Abstract: | This paper provides new evidence on how the adoption of artificial intelligence (AI) affects productivity and employment in Europe. Using matched EIBIS-ORBIS data on more than 12, 000 non-financial firms in the European Union (EU) and United States (US), we instrument the adoption of AI by EU firms by assigning the adoption rates of US peers to isolate exogenous technological exposure. Our results show that AI adoption increases the level of labor productivity by 4%. Productivity gains are due to capital deepening, as we find no adverse effects on firm-level employment. This suggests that AI increases worker output rather than replacing labor in the short run, though longer-term effects remain uncertain. However, productivity benefits of AI adoption are unevenly distributed and concentrate in medium and large firms. Moreover, AI-adopting firms are more innovative and their workers earn higher wages. Our analysis also highlights the critical role of complementary investments in software and data or workforce training to fully unlock the productivity gains of AI adoption. |
| Keywords: | artificial intelligence, firm productivity, Europe, digital transformation |
| JEL: | D22 J24 L25 O33 O47 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1325 |
| By: | Elert, Niklas (Institute of Retail Economics (HFI)); Henrekson, Magnus (Research Institute of Industrial Economics (IFN)) |
| Abstract: | Welfare services such as healthcare, elderly care, and education are key to ensuring quality of life generally, and vital for rural communities across urbanizing countries. While these sectors are largely tax-financed, several countries have established quasi-markets to achieve competition through private entry to unleash entrepreneurship, efficiency, and service provision innovation. The reforms notwithstanding, productivity improvements are modest, and the situation seems particularly bad in some rural communities. We argue that quasi-markets can only live up to expectations if the local institutional framework considers sectoral and local conditions. While competition and the profit motive are necessary conditions for local quasi-market entrepreneurship and innovation, they are not sufficient but require a set of complementary institutions that are epistemic in nature. These epistemic institutions enable users to make informed choices while simultaneously incentivizing entrepreneurs to compete and innovate along the dimensions that users value. Moreover, if the catchment area includes densely populated areas, rural communities may attract users from communities where costs are higher, thus creating new comparative advantages locally. As an illustration, we analyze the Swedish quasi-market for nursing homes for the elderly. |
| Keywords: | Entrepreneurship; Innovation; Innovation policy; Marketized care; Quasi-markets; Welfare services |
| JEL: | H42 H44 H75 I22 I28 L88 O31 |
| Date: | 2026–01–03 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:iuiwop:1549 |