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on Entrepreneurship |
| By: | Francesco D'Alessandro (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy); Enrico Santarelli (, Department of Economics, University of Bologna, Italy - Global Labor Organization (GLO), Essen, Germany); Marco Vivarelli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy – UNU-MERIT, Maastricht, The Netherlands – IZA, Bonn, Germany) |
| 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: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ctc:serie5:dipe0039 |
| By: | Martin Obschonka; Moren Levesque |
| Abstract: | The rapid expansion of AI adoption (e.g., using machine learning, deep learning, or large language models as research methods) and the increasing availability of big data have the potential to bring about the most significant transformation in entrepreneurship scholarship the field has ever witnessed. This article makes a pressing meta-contribution by highlighting a significant risk of unproductive knowledge exchanges in entrepreneurship research amid the AI revolution. It offers strategies to mitigate this risk and provides guidance for future AI-based studies to enhance their collective impact and relevance. Drawing on Akerlof's renowned market-for-lemons concept, we identify the potential for significant knowledge asymmetries emerging from the field's evolution into its current landscape (e.g., complexities around construct validity, theory building, and research relevance). Such asymmetries are particularly deeply ingrained due to what we term the double-black-box puzzle, where the widely recognized black box nature of AI methods intersects with the black box nature of the entrepreneurship phenomenon driven by inherent uncertainty. As a result, these asymmetries could lead to an increase in suboptimal research products that go undetected, collectively creating a market for lemons that undermines the field's well-being, reputation, and impact. However, importantly, if these risks can be mitigated, the AI revolution could herald a new golden era for entrepreneurship research. We discuss the necessary actions to elevate the field to a higher level of AI resilience while steadfastly maintaining its foundational principles and core values. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08890 |
| By: | Leogrande, Angelo; Drago, Carlo; Arnone, Massimo |
| Abstract: | The article explores the diffusion of online sales tools among Italian enterprises with at least ten employees, considering regional inequalities through methods that help address economic policy. The study gives an overall assessment of the adoption of e-commerce among Italian SMEs, using multiple methods that help to identify regional disparities and provide insight for policymakers. The data were obtained from the ISTAT-BES database. Analysis was applied using the k-Means machine learning algorithm by comparing the Silhouette coefficient vs. the Elbow method. The elbow method reveals greater expository capacity, and the optimal number of clusters equals 3. The econometric analysis used the following methods: Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Least Squares-WLS, and Dynamic Panels at 1 Stage. The results show that cultural and creative employment and regular internet users are positively associated with SMEs active in e-commerce while negatively associated with the family's availability of at least one computer and internet connection. Finally, the article compares different machine learning algorithms to predict the future value of SMEs active in e-commerce. The results are discussed critically. |
| Keywords: | e-Commerce, Small and Medium Enterprises, Regional Inequalities, Panel Data, k-Means, Machine-Learning. |
| JEL: | O3 O30 O31 O32 O33 O34 O38 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122115 |
| By: | Hiroshi Inokuma (Director and Senior Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: hiroshi.inokuma@boj.or.jp)); Juan M. Sánchez (Senior Economic Policy Advisor, Federal Reserve Bank of St. Louis (E-mail: juan.m.sanchez@stls.frb.org)) |
| Abstract: | A slowdown in population growth causes a decline in business dynamism by increasing the share of old businesses. But how does it affect productivity growth? We answer this question by extending a standard business dynamics model to include endogenous productivity growth. Theoretically, the growth rate of the size of surviving old businesses is a "sufficient statistic" for determining the direction and magnitude of the impact of population growth on productivity growth. Quantitatively, this effect is significant across balanced growth paths for the United States and Japan. TFP growth in the United States falls by 0.3 percentage points because of the slowing in population growth between 1970 and 2060. The same driving force produces a significantly bigger response in Japan. Despite the significant long-run effect, we discover that changes in TFP growth are slow in reaction to population growth changes due to two short-run counterbalancing factors. |
| Keywords: | population growth, economic growth, firms dynamics, demographics, productivity, innovation, TFP |
| JEL: | E20 J11 O33 O41 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ime:imedps:24-e-09 |
| By: | Sabien Dobbelaere (Vrije Universiteit Amsterdam); Michael D. König (Vrije Universiteit Amsterdam); Andrin Spescha (ETH Zurich); Martin Wörter (ETH Zurich) |
| Abstract: | The fraction of R&D active firms decreased in Switzerland but increased in the Netherlands from 2000-2016. This paper examines reasons for this divergence and its impact on productivity growth. Our micro-data reveal R&D concentration among high-productivity firms in Switzerland. Innovation support sustains firms’ R&D activities in both countries. Our structural growth model identifies the impact of innovation, imitation and R&D costs on firms’ R&D decisions. R&D costs gained importance in Switzerland but not in the Netherlands, explaining the diverging R&D trends. Yet, counterfactual analyses show that policies should prioritize enhancing innovation and imitation success over cost reduction to boost productivity growth. |
| Keywords: | R&D, innovation, imitation, R&D costs, policy, productivity growth, traveling wave. |
| Date: | 2023–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20230080 |
| By: | Fetzer, Thiemo (Warwick University and University of Bonn); Palmou, Christina (Office for National Statistics (ONS)); Schneebacher, Jakob (Competition and Markets Authority (CMA)) |
| Abstract: | We study how businesses adjust to significant rises in energy costs. This matters for both the current energy crisis and the longer-term shift towards Net Zero. Using firm-level real-time survey and administrative data backed by a pre-registered analysis plan, we examine how firms respond to the energy price shock triggered by Russia’s invasion of Ukraine along output, price, input, process and survival margins. We find that, on average, firms pass on some cost increases, build up cash reserves, and face higher debt, but do not yet see layoffs or bankruptcies. However, effects are highly heterogeneous by size and industry: for instance, small firms tend to increase cash reserves and prices, while large firms invest more in capital. We estimate separate elasticities for many small industry cells and subsequently use kmeans clustering techniques on the estimated effects to identify high-dimensional firm-adaptation archetypes. These estimates can help tailor firm support in the energy transition both in the short and the long term. More generally, the machinery developed in this paper enables policymakers to evaluate and adjust economic policy in near-real time. |
| Keywords: | energy price shock ; firm dynamics ; climate change ; high-dimensional analysis JEL Codes: D22 ; D24 ; H23 ; L11 ; O30 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:warwec:1517 |
| By: | Moleka, Pitshou Basikabio |
| Abstract: | In an era of rapid technological advancements, complex global challenges, and intense market competition, the ability to generate and scale innovative solutions has become a critical imperative for organizations, policymakers, and societies worldwide. However, the existing academic landscape has lacked a cohesive, multidisciplinary framework for comprehensively understanding the multifaceted nature of innovation. Innovationology, a newly established scientific discipline, aims to address this gap by providing a unifying, transdisciplinary approach to the study and practice of transformative innovation. This comprehensive article introduces Innovationology as a cutting-edge science that integrates insights from diverse fields, including management, psychology, sociology, economics, and technology studies. Innovationology posits that innovation is a multilayered, context-dependent phenomenon, shaped by the intricate interplay of individual, team, organizational, and ecosystem-level factors. By synthesizing the latest theoretical advancements and empirical evidence, this article presents a holistic model of Innovationology that illuminates the key determinants of radical, game-changing innovations capable of disrupting existing industries and creating new market spaces. The article delves deep into the individual cognitive, behavioral, and motivational drivers of innovativeness, the team dynamics and organizational structures that foster collaborative innovation, and the ecosystem-level characteristics that catalyze the emergence and scaling of transformative innovations. Importantly, the article explores the crucial role of contextual factors, such as socio-cultural norms, institutional support, and resource availability, in shaping innovation outcomes. This article also establishes the epistemological foundations of Innovationology, grounding it in a transdisciplinary, holistic, and pragmatic approach to knowledge generation. Innovationology embraces a pluralistic epistemology that acknowledges the complexity and context-dependence of innovation, drawing on diverse methodological approaches to capture the multifaceted nature of this phenomenon. Furthermore, the article outlines the object of Innovationology, which is to provide a comprehensive, evidence-based understanding of the drivers, processes, and outcomes of transformative innovation. Innovationology seeks to elucidate the multilevel determinants of innovation, the dynamic interplay between various factors, and the contextual influences that shape innovation trajectories. By establishing a unifying, transdisciplinary framework, Innovationology aims to bridge the gap between innovation theory and practice, empowering a wide range of stakeholders to unlock the transformative potential of innovation. Importantly, this article outlines the practical applications of Innovationology, providing comprehensive strategies and evidence-based interventions for cultivating innovative mindsets, designing innovation-conducive organizational systems, and navigating the challenges of innovative ecosystems. The implications of Innovationology for entrepreneurs, corporate leaders, policymakers, and innovation scholars are discussed in detail. By establishing Innovationology as a distinct, authoritative scientific discipline, this article sets the foundation for a more holistic, context-sensitive understanding of innovation and its multifaceted drivers. The insights generated by this new science can empower global organizations, institutions, and policymakers to address the complex, interconnected challenges of the 21st century through the strategic deployment of transformative innovations. |
| Date: | 2024–09–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:f3scj |