|
on Economics of Strategic Management |
|
Issue of 2025–07–28
eleven papers chosen by João José de Matos Ferreira, Universidade da Beira Interior |
| By: | Jun Cui |
| Abstract: | This study examines the relationship between AI-driven digital transformation and firm performance in Chinese industrial enterprises, with particular attention to the mediating role of green digital innovation and the moderating effects of human-AI collaboration. Using panel data from 6, 300 firm-year observations collected from CNRDS and CSMAR databases between 2015 and 2022, we employ multiple regression analysis and structural equation modeling to test our hypotheses. Our findings reveal that AI-driven digital transformation significantly enhances firm performance, with green digital innovation mediating this relationship. Furthermore, human-AI collaboration positively moderates both the direct relationship between digital transformation and firm performance and the mediating pathway through green digital innovation. The results provide valuable insights for management practice and policy formulation in the context of China's evolving industrial landscape and digital economy initiatives. This research contributes to the literature by integrating perspectives from technology management, environmental sustainability, and organizational theory to understand the complex interplay between technological adoption and business outcomes. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.11558 |
| By: | Shailender Kumar Hooda (Institute for Studies in Industrial Development, New Delhi) |
| Abstract: | Given the crucial role of enhancing investment in Research and Development (R&D) to drive innovation, improve competitive performance, and foster industrial growth through technological advancements, this study investigates the current status, quantum, and trends in R&D investment behaviour at the firm level within India’s organized manufacturing sector. By using unit-level data of Annual Survey of Industries (ASI), study provides nationally representative estimates of R&D propensity and intensity, distinguishing it from prior research constrained by data availability. The study assesses the contributions of small and medium size (SMS) versus large firms in R&D investments across various technology levels in the registered manufacturing sector. Our unique dataset makes this the first research to explore the impact of industry concentration and government incentives such as product-subsidy on R&D activities and intensity for SMS and large units using Cragg double-hurdle model and Heckman selection model, while accounting for other firm-level characteristics. Findings indicate that while overall R&D spending and activity levels are on the rise, though R&D intensity see a declining trend. Notably, SMS firms demonstrate higher R&D intensity in both low- and high-tech sectors compared to larger firms, though their intensity have been dwindling in the wake of pandemic, especially in high-tech industries segment. R&D spending in pharmaceutical industry now accounts for more than half of the overall organized manufacturing sector’s R&D, while the recent decline in R&D spending within the motor vehicle industry is concerning. The double hurdle regression analysis shows that larger firms, those with foreign capital, and those in high-tech industries are more likely to engage in R&D and invest more in it. Factors such as firm age and location in high industrial activity concentration areas also significantly influence R&D investment. Although the product subsidy coefficients were positive, they were less significant in impacting the R&D engagement likelihood, suggesting that while subsidies can support R&D, their direct impacts are often limited. However, firms receiving subsidies on a larger number of products experienced a significant positive influence on their R&D activities. For SMS firms, the results indicate that they may benefit more from subsidies and technology imports, pointing towards potential policy interventions to enhance their R&D efforts. |
| Keywords: | Manufacturing, R&D behaviour, R&D intensity, SMEs, Technology Intensity, Industrial Concentration, Product-Subsidy, India |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:sid:wpaper:285 |
| By: | Gibson, Gráinne; Lenihan, Helena; Perez-Alaniz, Mauricio; Rammer, Christian |
| Abstract: | Climate change can cause major challenges for Small and Medium-sized Enterprises (SMEs). Responding and adapting to such challenges is crucial, as SMEs are vital for driving economic growth and employment in most countries. Investing in R&D is a key way in which SMEs can build the capacities required for responding and adapting to climate change-related challenges. However, the extent to which such challenges affect SMEs' R&D activities remains a critical gap in existing knowledge. Using detailed firm-level data on 1, 730 SMEs in Ireland, our study is the first to explore this issue. We achieve this, using information on SMEs' climate changerelated challenges, from a new module of the 2018-2020 wave of the Irish part of the Community Innovation Survey (CIS), the Innovation in Irish Enterprises Survey (IIE). By combining a matching approach with probit regression analysis, we find that climate changerelated challenges can increase the probability of SMEs investing in R&D. Such challenges can also increase the probability of SMEs engaging in continuous, as opposed to occasional R&D. Based on our findings, the above impacts are mainly driven by climate change, resulting in higher costs/input prices. Our study highlights the importance of R&D for SMEs to adapt and respond to climate change and provides critical insights for SMEs and policymakers alike. |
| Keywords: | Climate change-related challenges, small and medium sized enterprises, research and development, climate change adaptation, climate change mitigation |
| JEL: | Q54 Q55 O32 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:319901 |
| By: | Niccolò Murtas (University of Ferrara) |
| Abstract: | This study estimates an aggregate green knowledge production function (GKPF) for 19 OECD countries from 1981 to 2012, using panel-data econometric methods to address spatial spillovers and unobserved heterogeneity. Both Cobb-Douglas and translog functional forms are evaluated with multiple estimators, including standard fixed and random effects models, pooled and mean group common correlated effects (CCE) estimators, and random-trend models to account for shared upward trends among variables. The regression analysis examines the relationship between green patenting and key determinants such as R&D expenditure, human capital, and environmental policy indicators. The results consistently show a robust positive effect of domestic R&D, whereas the impacts of other factors exhibit greater variability. Methodologically, the findings highlight the sensitivity of coefficient estimates to unobserved heterogeneity and the choice of functional form. |
| Keywords: | Green innovation, knowledge production function, panel data, spatial spillovers |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:srt:wpaper:0725 |
| By: | Pelissier Pierre-Mathieu (European Commission - JRC); Grabowska Marcelina (European Commission - JRC); Bergamini Michela (European Commission - JRC) |
| Abstract: | This report presents a patent landscape analysis investigating the innovation trends within the industrial biotechnology (IB) sector from 2015 to 2020. The study's primary objective is to identify the geographical hotspots of innovation, the key players, and the role of different types of organizations in driving technological advancements in IB. By employing a methodology that includes data retrieval through the Technology Innovation Monitoring (TIM) tool and careful selection of keywords and Cooperative Patent Classification (CPC) terms, the report categorizes patents across five technological areas pertinent to IB. The geographical scope of the analysis encompasses major global players as well as the European Union, providing a broad view of the innovation landscape. The report also introduces an online dashboard to facilitate further analysis and exploration of the data. This study serves as a resource for policymakers, industry stakeholders, and researchers, offering insights that can inform strategic planning and decision-making in the evolving field of industrial biotechnology. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc139154 |
| By: | Sara Amoroso; Randolph L. Bruno; Laura Magazzini |
| Abstract: | This paper identifies the dichotomous role (bright and dark sides) of Intellectual Property Rights (IPR) protection on labor productivity among highly innovative globalised firms. The role of appropriability conditions -such as IPR protection- as "Schumpeterian" incentive to innovation has been largely explored in the empirical literature. In this paper, we contribute to this strand explore the role of appropriability conditions on firm labor productivity under different configurations of R&D activities in highly globalized companies. In line with the literature, we show that labor, capital and R&D investments lead to productivity gains, and that the strength of the patent system the firm is embedded into is positively linked to the firm’s labor productivity too. We call this the 'bright side' of IPR. However, stronger intellectual property rights might have a detrimental effect on the R&D returns, which appear to be maximized around the median level of IPR protection. In other words, too much protection might actually reduce R&D returns, again in line with the "Schumpeterian prediction". Then, we call this the ‘dark side’ of IPR. To our knowledge, this is the first paper highlighting such dichotomy (bright and dark sides of IPR) on a purpose-built high-quality database of globalized firms, which tend to be the most innovative firms in the world. |
| Keywords: | panel data, appropriability, productivity |
| Date: | 2025–07–24 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2025/26 |
| By: | Lasarte Lopez Jesus (European Commission - JRC); Gonzalez Hermoso Hugo; M'barek Robert (European Commission - JRC) |
| Abstract: | Life sciences-related sectors play a vital role in addressing EU challenges, driving innovation in key areas like healthcare, biotechnology, and agriculture to enhance competitiveness, sustainability, and strategic autonomy. This policy brief examines the socioeconomic relevance, structure, and trends of Life Sciences sectors using three key economic indicators: employment, value added and R&D business expenditure. The analysis shows that Life Sciences sectors are crucial to the EU economy, accounting for 9.4% of GDP and employing 29 million people. These sectors have also driven economic growth in recent years, with increasing GDP contributions and job creation in productive sectors, and offer high growth potential and innovation capacity to address EU challenges. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc142396 |
| By: | Reher, Leonie; Thomä, Jörg; Bizer, Kilian |
| Abstract: | This paper advances the empirical measurement of the Doing-Using-Interacting (DUI) mode of innovation, based on the conceptual framework of Alhusen et al. (2021) and its survey-based operationalization of Reher et al. (2024b). Using data from German SMEs, we examine whether the three-dimensional structure of DUI learning theorized in the literature can be mirrored empirically. Exploratory factor analysis (EFA) confirms this latent structure by identifying three main learning processes: (1) DUI internal (learning-by-doing and internal interaction), (2) DUI user-driven (learning-by-using), and (3) DUI external (learning-by-externalinteraction). However, some factor loadings are problematic, suggesting that not all of the original indicators are suitable for measuring the DUI mode of innovation. Secondly, building on the latent structure identified through EFA, short scales of various lengths are developed using Ant Colony Optimization (ACO) to address practical constraints in innovation surveys. This provides a starting point for the further development of DUI innovation indicators that are particularly suited to less RD-intensive innovation contexts, such as small firms, low-tech sectors, and lagging regions, as well as corresponding short scales. |
| Abstract: | Diese Studie verbessert die empirische Messung des Doing-Using-Interacting (DUI)-Innovationsmodus auf Grundlage des konzeptionellen Rahmens von Alhusen et al. (2021) und der umfragebasierten Operationalisierung von Reher et al. (2024b). Anhand von Daten deutscher KMU wird untersucht, ob sich die in der Literatur theoretisch hergeleitete dreidimensionale Struktur des DUI-Lernens auch empirisch abbilden lässt. Eine explorative Faktorenanalyse (EFA) bestätigt diese latente Struktur, indem sie drei zentrale Lernprozesse identifiziert: 1. DUI internal: beschreibt die innerbetriebliche Bedeutung von Schulungen, Fehlerkultur, (informellen) Wissensaustauschs oder des Personalmanagements im Innovationsprozess. 2. DUI user-driven: bezieht sich auf die Einbindung von Kundenwissen in Innovationen durch Kooperation, Kundenkontakt oder Produktspezifikationen. 3. DUI external: umfasst innovationsbezogenes Lernen durch den Austausch mit Zulieferern, Wettbewerbern, Akteuren innerhalb und außerhalb des eigenen Sektors, Beratungsunternehmen und öffentlichen Institutionen sowie die Bedeutung von Netzwerken und Branchenverbänden. Einige Faktorladungen sind jedoch problematisch, was darauf hindeutet, dass nicht alle ursprünglichen Indikatoren zur Messung des DUI-Innovationsmodus geeignet sind. Darüber hinaus werden - basierend auf der durch die EFA identifizierten latenten Struktur - mittels Ant Colony Optimization (ACO) Kurzskalen unterschiedlicher Länge entwickelt, um praktischen Einschränkungen in Innovationsumfragen zu begegnen. Dies stellt einen Ausgangspunkt für die Weiterentwicklung von DUI-Innovationsindikatoren dar, die insbesondere für weniger F&E-intensive Innovationskontexte geeignet sind - etwa in kleinen Unternehmen, in Low-Tech-Sektoren oder in strukturschwachen Regionen - sowie für entsprechende Kurzskalen. |
| Keywords: | innovation measurement, innovation indicator, modes of innovation, SMEs |
| JEL: | O30 O31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifhwps:321860 |
| By: | Philippe Jean-Baptiste (LEST - Laboratoire d'Economie et de Sociologie du Travail - AMU - Aix Marseille Université - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | This research explores how Generative Artificial Intelligence (GAI) transforms the roles and competencies of middle managers. Grounded in activity theory, it examines organizational tensions, particularly between Bottom-Up innovation and centralized governance. A qualitative methodology, based on 60 semi-structured interviews conducted across large enterprises, medium-sized enterprises, and small businesses, investigates these dynamics in diverse contexts.Preliminary findings reveal that middle managers play a pivotal role in adopting GAI, often bypassing formal frameworks through Shadow IT. They are emerging as facilitators of change, requiring enhanced human and conceptual skills to interpret AI tools and manage organizational tensions effectively.This research proposes practical recommendations to balance innovation with compliance while strengthening the role of middle managers in technological transitions. Feedback is sought on analysing tensions, identifying managerial competencies, and ensuring the transferability of results. |
| Abstract: | Cette recherche explore comment l'intelligence artificielle générative (GAI) transforme les rôles et les compétences des cadres intermédiaires. La théorie de l'activité, qui observe les tensions organisationnelles, en particulier entre l'innovation ascendante et la gouvernance top-down. Une méthodologie qualitative, basée sur 60 entretiens semi-structurés menés auprès de grandes entreprises, de moyennes entreprises et de petites entreprises, étudie ces dynamiques dans divers contextes. Les résultats préliminaires révèlent que les managers intermédiaires jouent un rôle central dans l'adoption de l'IAG, contournant souvent les cadres formels grâce au Shadow IT. Ils émergent comme des facilitateurs du changement, nécessitant des compétences humaines et conceptuelles améliorées pour interpréter les outils d'IA et gérer efficacement les tensions organisationnelles. Cette recherche propose des recommandations pratiques pour équilibrer l'innovation et la conformité tout en renforçant le rôle des cadres intermédiaires dans les transitions technologiques. Des commentaires sont recherchés sur l'analyse des tensions, l'identification des compétences managériales et la garantie de la transférabilité des résultats. |
| Keywords: | Generative Artificial Intelligence (GAI), Middle managers, Organizational transformation, activity theory, bottom-up innovation, Shadow IT, managerial competencies, skilling, technological adoption |
| Date: | 2025–06–15 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05113172 |
| By: | Hanol Lee; Jong-Wha Lee |
| Abstract: | This study develops a novel cross-country measure of higher education quality by leveraging the robust relationship between institution-level indicators--such as faculty-to-student ratios and global university rankings--and the earnings of graduates employed overseas. Using U.S. microdata, it shows that global rankings are strongly correlated with key quality dimensions, including research performance, teaching environment, enrollment size, international outlook, and student selectivity. Building on this relationship, a country-level index of college education quality is constructed for 98 countries, capturing variations in institutional characteristics weighted by their estimated effects on graduate earnings. To examine macroeconomic impacts, the study estimates cross-country regressions of GDP per worker, resident patenting, and R&D expenditures. An instrumental variable strategy--exploiting geographic proximity to global academic hubs--is used to address potential endogeneity. The results show that tertiary education quality has a large and statistically significant effect on all three outcomes, underscoring its role in long-run economic development and innovation capacity. |
| Keywords: | education quality, human capital, economic development, innovation, college education, university rankings |
| JEL: | I23 I25 J24 O15 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-41 |
| By: | Dahlke, Johannes; Schmidt, Sebastian; Lenz, David; Kinne, Jan; Dehghan, Robert; Abbasiharofteh, Milad; Schütz, Moritz; Kriesch, Lukas; Hottenrott, Hanna; Kanilmaz, Umut Nefta; Grashof, Nils; Hajikhani, Arash; Liu, Lingbo; Riccaboni, Massimo; Balland, Pierre-Alexandre; Wörter, Martin; Rammer, Christian |
| Abstract: | This paper introduces the WebAI paradigm as a promising approach for innovation studies, business analytics, and informed policymaking. By leveraging artificial intelligence to systematically analyze organizational web data, WebAI techniques can extract insights into organizational behavior, innovation activities, and inter-organizational networks. We identify five key properties of organizational web data (vastness, comprehensiveness, timeliness, liveliness, and relationality) that distinguish it from traditional innovation metrics, yet necessitate careful AI-based processing to extract scientific value. We propose methodological best practices for data collection, AI-driven text analysis, and hyperlink network modeling. Outlining several use cases, we demonstrate how WebAI can be applied in research on innovation at the micro-level, technology diffusion, sustainability transitions, regional development, institutions and innovation systems. By discussing current methodological and conceptual challenges, we offer several propositions to guide future research to better understand i) websites as representations of organizations, ii) the systemic nature of digital relations, and iii) how to integrate WebAI techniques with complementary data sources to capture interactions between technological, economic, societal, and ecological systems. |
| Keywords: | web data, artificial intelligence, innovation studies, research methods |
| JEL: | C81 C45 B4 O3 R1 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:319890 |