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on Economics of Strategic Management |
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Issue of 2026–03–30
ten papers chosen by João José de Matos Ferreira, Universidade da Beira Interior |
| By: | Drydakis, Nick |
| Abstract: | Artificial intelligence (AI) is increasingly recognised as a key driver of business innovation, yet its adoption among small and medium-sized enterprises (SMEs) varies considerably. This study examines whether AI Capital, defined as AI-related knowledge, skills and capabilities, is associated with business innovation among SMEs in England. Using a two-wave longitudinal panel dataset comprising 504 observations from SMEs collected in 2024 and 2025, the study develops and validates a 45-item AI Capital of Business scale. Business innovation is measured across five dimensions: product and service innovation, process innovation, technology adoption, market and customer engagement, and organisational culture and strategy. Regression models, including pooled OLS, Random Effects, and Fixed Effects specifications, are employed. The findings reveal a robust positive association between AI Capital and business innovation across all model specifications. This association holds across all business innovation dimensions and remains consistent for SMEs with differing levels of financial performance, size, and operational maturity. Each component of AI Capital independently exhibits a positive association with business innovation outcomes. The results highlight the central role of AI Capital in enabling SMEs to translate AI adoption into tangible business innovation. From a policy perspective, the findings indicate the value of targeted interventions that prioritise AI upskilling, organisational capability development, and accessible support mechanisms to promote inclusive and sustainable AI-driven business innovation among SMEs. |
| Keywords: | Artificial Intelligence, Artificial Intelligence Capital, Business Innovation, Innovation, SMEs |
| JEL: | O31 O33 O32 L26 L25 M15 D83 J24 O14 O39 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1723 |
| By: | Davide Antonioli; Elisa Chioatto; Giovanni Guidetti; Riccardo Leoncini; Mariele Macaluso |
| Abstract: | This paper analyses how firms' skill development strategies affect their propensity to introduce innovation. We develop an adjustment-cost framework that links human capital theory and institutionalist and evolutionary approaches, considering innovation as an activity that entails costs in labour adjustment arising either from the training activities of workers or the recruitment of skilled employees. Using a two-wave panel of Italian manufacturing firms observed in 2017-2018 and 2019-2020, we analyse firms' adoption of total, product, process, and circular innovation as a function of internal training practices and of external skills acquisition. Overall, the empirical analysis confirms the expected positive relationship between training and innovation, while also revealing important nuances in the workforce upskilling strategies required for different types of innovation. Moreover, while training activities and skills development are essential across all forms of innovation, our findings indicate that internal training is particularly effective in supporting the implementation of circular innovations. By contrast, external recruitment appears to be consistently necessary whenever innovations are introduced, regardless of their type. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.05153 |
| By: | Peng Hu'An (Guangxi Normal University); Fateh Saci (CHROME - Détection, évaluation, gestion des risques CHROniques et éMErgents (CHROME) - Nîmes Université - UNIMES - Nîmes Université, UMay - Université de Mayotte (UMay)); Javid Iqbal (CUI - COMSATS University Islamabad); Mohamad Ahmad (LARGEPA - Laboratoire de recherche en sciences de gestion Panthéon-Assas - Université Paris-Panthéon-Assas); Wafa Ghardallou (Princess Nourah Bint Abdulrahman University); Ubaldo Comite (Giustino Fortunato University) |
| Abstract: | Amongst escalating environmental challenges, organizations are increasingly adopting leadership approaches that advance sustainability-oriented outcomes. This study investigates the influence of Green Servant Leadership (GSL) on Green Innovation (GI), emphasizing the mediating role of Tacit Green Knowledge (TK) and the moderating effect of Organizational Green Culture (OC). Drawing on empirical data collected from China's manufacturing sector and employing structural equation modeling via SmartPLS, the results demonstrate that GSL significantly fosters TK, which subsequently promotes GI. Furthermore, the moderation analysis indicates that OC positively strengthens the relationship between GSL and TK (β = 0.121, T = 3.562, p < 0.001), suggesting that the impact of green leadership on knowledge-sharing behaviors is amplified in organizations that cultivate a strong green culture. This moderated mediation effect implies that organizational culture not only enhances the dissemination of tacit green knowledge but also strengthens the indirect influence of leadership on innovation. |
| Keywords: | Moderated Mediation, Knowledge-Based, Organizational Green Culture, Green Innovation, Tacit Green Knowledge, Leadership |
| Date: | 2026–01–20 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05551357 |
| By: | Salvatore Viola (Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona, Spain; AQR-IREA, Spain.); Ernest Miguelez (Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona, Spain; AQR-IREA, Spain.); Rosina Moreno (Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona, Spain; AQR-IREA, Spain.); Davide Consoli (INGENIO, Universitat Politècnica de València, Spain; CSIC-UPV, Spain); François Perruchas (Universitat Politècnica de València, Spain.) |
| Abstract: | One important factor in addressing climate change is the development and deployment of environmental-related, or green, technologies (GT). Environmental-related technologies are distinct, requiring specific conditions to be developed which vary depending on their relative level of technological maturity. Recent studies have focused on the role of migrant inventors in creating these conditions and spurring regional diversification into new technological domains. Regional diversification helps regions avoid lock-in and even escape fossil fuel dependencies. While the contribution of migrants to science and innovation is well documented, less attention has been given to migrants and diversification, especially in the case of GT and along the technological life cycle. In this study, we investigate the role of US-based migrant inventors in regional GT diversification using patent data from the USPTO between the year 1990 and 2012. We find that migrant inventors are positively associated with regional GT diversification, partly as a result of their previous patenting experience as well as the specializations of their countries of origin. With regard to the technological life cycle, while geographically diffused technologies rely on corresponding inventor experience, emergent technological diversification benefits from inventors from specialized countries. These findings highlight the bridging role that migrant inventors in international knowledge transfer and their importance in regional diversification in particular environmental-related technologies. |
| Keywords: | Regional Diversification; Green Technology; Immigration; Technological Life Cycle. JEL classification: O33; Q55; J61; R11. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:ira:wpaper:202517 |
| By: | Julia Müller; Thorsten Upmann |
| Abstract: | This paper develops a dynamic model in which the productivity of joint research governs strategic investment timing in innovation races. Departing from the standard assumption that discovery rates scale proportionally with the number of active firms, we allow research to exhibit decreasing or increasing returns, thereby endogenizing the aggressiveness of innovation competition. We show that returns to joint research determine whether innovation races exhibit preemption or coordination. When research efforts are substitutes, follower entry is unattractive, generating a first-mover advantage and a preemption equilibrium. When complementarities are sufficiently strong, the gains from early investment vanish and firms invest simultaneously. The model thus identifies a regime shift in innovation races: competition accelerates investment under decreasing returns but promotes coordinated entry under increasing returns. These findings highlight the research technology as a central determinant of market dynamics and provide a unified perspective on heterogeneous patterns of innovation. |
| Keywords: | innovation races, R&D competition, strategic investment timing, preemption and coordination, research complementarities |
| JEL: | O31 D81 C73 L13 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12552 |
| By: | Konstantin Remke (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School); Sönke Mestwerdt (Alliance MBS - Alliance Manchester Business School - University of Manchester [Manchester]); Matthias Mrożewski (ESCP Europe Campus Berlin - ESCP Europe - Ecole Supérieure de Commerce de Paris); Maximilian Tigges (ESCP Europe Campus Berlin - ESCP Europe - Ecole Supérieure de Commerce de Paris); René Mauer (ESCP Europe Campus Berlin - ESCP Europe - Ecole Supérieure de Commerce de Paris) |
| Abstract: | The circular economy (CE) has emerged as a promising paradigm to address environmental challenges through resource efficiency, product life cycle transformation, and business model innovation. Yet, implementing circular business models remains challenging due to the complexity of coordinating stakeholders and managing large volumes of data, especially across the different life cycle phases. Artificial intelligence (AI) offers great potential to address these challenges. However, prior research has predominantly focused on a generic and undifferentiated application of AI within the CE, neglecting the complexity and diversity across life cycle phases. Our study seeks to address this. Drawing on the external enablement framework, we conduct a qualitative study comprising 57 semi‐structured interviews with domain experts and AI‐based ventures operating in the CE. In demonstrating how AI facilitates business model innovation by enabling navigation through circular product life cycles, our findings advance three key areas. We extend the external enablement framework by showing that external enablers can interact synergistically. Specifically, we develop a model that illustrates how the underlying dynamics of the CE and AI jointly enable business model innovation and subsequent new venture creation through complementary mechanisms, a dynamic we term dual enablement . We show how AI enhances life cycle efficiency, fosters systems interconnectivity, and supports holistic decision‐making, engendering two novel categories of business models, which we coin AI‐based business models for complexity navigation and dual enablement. |
| Keywords: | Artificial intelligence, Circular economy, Business model innovation, External enablement, Grand challenges |
| Date: | 2026–01–22 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05547705 |
| By: | Diego Ocampo-Corrales (AQR-IREA, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona, Spain.); Rosina Moreno (AQR-IREA, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona, Spain.) |
| Abstract: | This paper investigates the role of regions’ recombinatorial technological capacity in shaping the technological space. To do so, we identify novel combinations of technologies and track their evolution by tracing all subsequent inventions that incorporate the same combination. Building on the concepts of relatedness and geographical proximity, we focus on the relevance of the technological antecedents of a pair of technologies combined for the first time in determining their success. This is due through the estimation of the likelihood of a new technological combination eventually becoming embedded within the broader knowledge space. Using patent data from 1976 to 2022 in the case of the European regions, we find strong evidence that a higher degree of relatedness between the technological antecedents of the two combined technologies significantly increases the likelihood that the combination will be reused in future inventions. Additionally, we find that the success of a new combination also benefits from the presence of dissimilar knowledge—not directly involved in the combination’s antecedents but accessible within the surrounding technological environment. In these cases, the greater the relatedness between the new invention’s antecedents and the broader regional knowledge base, the more likely it is to generate a high number of follow-on inventions and contribute meaningfully to the formation of the technological space. |
| Keywords: | New Combination of Technologies; Regional Innovation; European Regions; Recombination Capacity; Knowledge Space; Technological Antecedents. JEL classification: O18; O31; O33; R11. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ira:wpaper:202526 |
| By: | RAHAL, Imen; Khalifa, Zayed |
| Abstract: | Artificial Intelligence (AI) has become one of the most influential drivers of economic transformation in the 21st century. By boosting productivity, optimizing business processes, supporting innovation, and enabling the emergence of new industries, AI contributes significantly to long-term economic growth. However, this technological shift also brings risks such as labor market disruption, inequality, skill mismatches, and regulatory gaps. This article examines the relationship between AI adoption and economic growth, highlighting the mechanisms through which AI stimulates economic performance, the structural challenges it generates, and strategies needed to ensure inclusive and sustainable growth. The paper draws on recent literature, economic reports, and empirical studies to offer a comprehensive perspective on the future of AI-driven economic expansion. |
| Keywords: | Artificial Intelligence, Economic Growth, Productivity, Innovation, Labor Market, Digital Transformation.; Artificial Intelligence, Economic Growth, Productivity, Innovation, Labor Market, Digital Transformation. |
| JEL: | O4 |
| Date: | 2025–10–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127061 |
| By: | RAHAL, Imene; KHALIFA, zayed |
| Abstract: | The Blue Economy represents a sustainable and integrated approach to the utilization of ocean and marine resources, aiming to foster economic growth, enhance human livelihoods, and preserve the health of marine ecosystems. As oceans play a critical role in global food security, transportation, energy production, and climate regulation, ensuring their sustainable management has become a global priority. In recent years, rapid advances in Information Technology (IT) have played a transformative role in reshaping how marine resources are monitored, managed, and utilized. Technologies such as big data analytics, artificial intelligence, satellite systems, and the Internet of Things enable real-time data collection, predictive modeling, and informed decision-making across marine sectors. This article explores the intersection between the Blue Economy and Information Technology by highlighting key digital tools, real-world applications, as well as the benefits and challenges associated with digital transformation. It argues that embracing digital innovation is essential for achieving a sustainable, resilient, and inclusive Blue Economy capable of addressing environmental pressures while supporting long-term economic development. |
| Keywords: | Blue Economy, Information Technology, Sustainable Development |
| JEL: | O3 |
| Date: | 2025–10–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127349 |
| By: | Ralf Martin; Arjun Shah; Anna Valero; Dennis Verhoeven |
| Abstract: | Quantifying spillovers from scientific knowledge to technology is important for understanding the social returns to science and for designing policy. A key challenge is how to credit scientific work with the value generated in downstream technologies when ideas diffuse through chains of follow-on research. We propose a new measure - Science Rank - that uses the combined patent and paper citation network to assign a share of the private value of patented inventions to the scientific papers they directly or indirectly rely on. Validated against various types of scientific awards, the measure substantially outperforms direct patent-to-paper citation counts in identifying influential science. We document large heterogeneity in spillovers across countries, disciplines, and institutions. The US emerges from our analysis as a powerhouse of science spillovers, benefiting both domestic and foreign technology development. We apply our methodology to examine how different countries and individual institutions contribute to innovation that addresses global challenges such as climate change or more equal economic development. We find that a relatively large share of the total value generated by research in Lower and Middle Income Country (LMIC) feeds into climate change related innovation. We also highlight countries and institutions that are making particular contributions to LMIC innovation. |
| Keywords: | Technological change, growth, patents, spillovers, climate change, economic development |
| Date: | 2026–03–18 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2165 |