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on Economics of Strategic Management |
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Issue of 2024‒09‒16
eight papers chosen by João José de Matos Ferreira, Universidade da Beira Interior |
| By: | D’Alessandro, Francesco (Università Cattolica del Sacro Cuore); Santarelli, Enrico (University of Bologna); Vivarelli, Marco (Università Cattolica del Sacro Cuore) |
| Abstract: | In this paper we integrate the insights of the Knowledge Spillover Theory of Entrepreneurship and Innovation (KSTE+I) with Schumpeter's idea that innovative entrepreneurs creatively apply available local knowledge, possibly mediated by Marshallian, Jacobian and Porter spillovers. In more detail, in this study we assess the degree of pervasiveness and the level of opportunities brought about by AI technologies by testing the possible correlation between the regional AI knowledge stock and the number of new innovative ventures (that is startups patenting in any technological field in the year of their foundation). Empirically, by focusing on 287 Nuts-2 European regions, we test whether the local AI stock of knowledge exerts an enabling role in fostering innovative entry within AI-related local industries (AI technologies as focused enablers) and within non AI-related local industries, as well (AI technologies as generalised enablers). Results from Negative Binomial fixed-effect and Poisson fixed-effect regressions (controlled for a variety of concurrent drivers of entrepreneurship) reveal that the local AI knowledge stock does promote the spread of innovative startups, so supporting both the KSTE+I approach and the enabling role of AI technologies; however, this relationship is confirmed only with regard to the sole high-tech/AI-related industries. |
| Keywords: | KSTE+I, Artificial Intelligence, innovative entry, enabling technologies |
| JEL: | O33 L26 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17206 |
| By: | Saïd Balhadj (ENCGT - Ecole Nationale de Commerce et de Gestion de Tanger - UAE - Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan]); Maryam EL MOUDDEN (ENCGT - Ecole Nationale de Commerce et de Gestion de Tanger - UAE - Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan]) |
| Keywords: | innovation organisationnelle l'avantage compétitif performance organisationnelle PME exportatrice marocaine Classification JEL : O31 Organizational innovation competitive advantage organizational performance Moroccan-exporting SMEs JEL Classification: O31 Paper type: Empirical research, innovation organisationnelle, l'avantage compétitif, performance organisationnelle, PME exportatrice marocaine, competitive advantage, organizational performance, Moroccan-exporting SMEs |
| Date: | 2022–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04653608 |
| By: | Giacomo Damioli; Vincent Van Roy; Daniel Vertesy; Marco Vivarelli |
| Abstract: | Artificial intelligence (AI) is emerging as a transformative innovation with the potential to drive significant economic growth and productivity gains. This study examines whether AI is initiating a technological revolution, signifying a new technological paradigm, using the perspective of evolutionary neo-Schumpeterian economics. Using a global dataset combining information on AI patenting activities and their applicants between 2000 and 2016, our analysis reveals that AI patenting has accelerated and substantially evolved in terms of its pervasiveness, with AI innovators shifting from the ICT core industries to non-ICT service industries over the investigated period. Moreover, there has been a decrease in concentration of innovation activities and a reshuffling in the innovative hierarchies, with innovative entries and young and smaller applicants driving this change. Finally, we find that AI technologies play a role in generating and accelerating further innovations (so revealing to be “enabling technologies”, a distinctive feature of GPTs). All these features have characterised the emergence of major technological paradigms in the past and suggest that AI technologies may indeed generate a paradigmatic shift. |
| Keywords: | Artificial Intelligence, Patents, Structural Change, Technological Paradigm |
| Date: | 2024–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:746877 |
| By: | Tricia Cleland Silva (Helsinki Metropolia University of Applied Sciences); Mika Hämäläinen (Helsinki Metropolia University of Applied Sciences) |
| Abstract: | This paper investigates the intersection of sustainability strategies and artificial intelligence (AI) in Human Resource Management (HRM) with a Finnish University of Applied Sciences as its case study. Drawing from a multi-level framework, the paper explores and discusses the interplay of barriers and drivers shaping sustainability practices in teaching and the potential of leveraging AI for consistent messaging and accountability to the organizational goals. The historical evolution of AI in HRM is presented with an emphasis on the need for strategic frameworks. The research design uses a cross-disciplinary approach, focusing on case study analyses within the educational context. Preliminary findings reveal a gap in translating institutional and strategic sustainability capabilities into operational practices, prompting the introduction of AI plugins for enhanced accountability and transparency in the digital learning platform. The study concludes with recommendations for future research, emphasizing the potential of AI in HRM system and practices to drive sustainability in higher education. |
| Keywords: | changing nature of work creativity and innovation HR and organizational strategy, changing nature of work, creativity and innovation, HR and organizational strategy |
| Date: | 2024–08–09 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04665987 |
| By: | Dirk Czarnitzki; Malte Prüfer |
| Abstract: | This paper investigates the impact of Public Procurement of Innovation (PPI) and Research and Development (R&D) grants on firms' R&D investment using data from Belgian R&D-active firms over the past decade. Our empirical analysis robustly reveals a non-negligible crowding-out effect between the two instruments, suggesting a substitutive relationship. While each policy individually positively influences R&D investment, their combined implementation diminishes their effectiveness. These results challenge prevailing evidence and emphasize the need for a careful policy implementation, raising policymakers’ awareness against a blanket increase in innovation policies without considering potential interactions. |
| Keywords: | Public procurement of innovation, Research and Development, Econometric policy evaluation, Crowding-out |
| Date: | 2024–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:746875 |
| By: | Lukas Rosenberger; W. Walker Hanlon; Carl Hallmann |
| Abstract: | How did Britain sustain faster rates of economic growth than comparable European countries, such as France, during the Industrial Revolution? We argue that Britain possessed an important but underappreciated innovation advantage: British inventors worked in technologies that were more central within the innovation network. We offer a new approach for measuring the innovation network using patent data from Britain and France in the late-18th and early-19th century. We show that the network influenced innovation outcomes and demonstrate that British inventors worked in more central technologies within the innovation network than French inventors. Drawing on recently developed theoretical tools, and using a novel estimation strategy, we quantify the implications for technology growth rates in Britain compared to France. Our results indicate that the shape of the innovation network, and the location of British inventors within it, explains an important share of the more rapid technological change and industrial growth in Britain during the Industrial Revolution. |
| JEL: | N13 O30 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32875 |
| By: | Felipe A. Csaszar; Harsh Ketkar; Hyunjin Kim |
| Abstract: | This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms. We illustrate how AI could augment existing SDM tools and provide empirical evidence from a leading accelerator program and a startup competition that current Large Language Models (LLMs) can generate and evaluate strategies at a level comparable to entrepreneurs and investors. We then examine implications for key cognitive processes underlying SDM -- search, representation, and aggregation. Our analysis suggests AI has the potential to enhance the speed, quality, and scale of strategic analysis, while also enabling new approaches like virtual strategy simulations. However, the ultimate impact on firm performance will depend on competitive dynamics as AI capabilities progress. We propose a framework connecting AI use in SDM to firm outcomes and discuss how AI may reshape sources of competitive advantage. We conclude by considering how AI could both support and challenge core tenets of the theory-based view of strategy. Overall, our work maps out an emerging research frontier at the intersection of AI and strategy. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.08811 |
| By: | Natália Barbosa (School of Economics and Management, University of Minho) |
| Abstract: | The adoption of new digital technologies offers new opportunities and has the scope to engender positive effects on firms’ expansion and success in international markets. This paper examines the main factors driving the adoption of Artificial Intelligence (AI) and AI-related digital technologies that enable the Industry 4.0 transformation and whether these new generation of digital technologies affect exporting performance at firm level. Using a rich and representative sample of Portuguese firms over the period 2014-2020, the estimated results suggest that firm’s ex-ante performance, digital infrastructures and in-house ICT skills are the main drivers of digitalisation. However, conditional to ex-ante firm’s performance, there are heterogenous effects on exporting performance across digital technologies and across industries. Moreover, there is evidence of positive selection towards large firms, casting doubts on the inclusiveness of the adoption process and the performance effects of AI and AI-related technologies. |
| Keywords: | Artificial Intelligence, Industry 4.0 enabling digital technologies, firms’ exporting performance |
| JEL: | L20 H81 L25 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:mde:wpaper:183 |