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on Economics of Strategic Management |
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Issue of 2026–08–24
twelve papers chosen by João José de Matos Ferreira, Universidade da Beira Interior |
| By: | Hunt, Jennifer; Cockburn, Iain; Bessen, James |
| Abstract: | Using our own data on Artificial Intelligence publications merged with Burning Glass vacancy data for 2007-2019, we investigate whether online vacancies for jobs requiring AI skills grow more slowly in U.S. locations farther from pre-2007 AI innovation hotspots. We find that a commuting zone which is an additional 200km (125 miles) from the closest AI hotspot has 17% lower growth in AI jobs' share of vacancies. This is driven by distance from AI papers rather than AI patents. Distance reduces growth in AI research jobs as well as in jobs adapting AI to new industries, as evidenced by strong effects for computer and mathematical researchers, developers of software applications, and the finance and insurance industry. 20% of the effect is explained by the presence of state borders between some commuting zones and their closest hotspot. This could reflect state borders impeding migration and thus flows of tacit knowledge. Distance does not capture difficulty of in-person or remote collaboration nor knowledge and personnel flows within multi-establishment firms hiring in computer occupations. |
| Keywords: | Artificial intelligence; Innovation and invention; Technology diffusion; Technology adoption |
| JEL: | O33 R12 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19537 |
| By: | Manoj Kumar; Prashanth BS; Ariful Hoque; Nasser Al Muraqab; Immanuel Azaad Moonesar; Udo Christian Braendle; Ananth Rao |
| Abstract: | This study examines the dynamics of technology transfer readiness and financial innovation capability transitions across the expanded BRICS economies, benchmarked against advanced innovation systems through explainable AI. Using a composite Innovation Capability Development-Readiness index (ICDI) constructed through principal component analysis, the paper evaluates the structural conditions enabling knowledge diffusion, industrial upgrading, and financial innovation ecosystem development. A Markov transition framework is employed to analyse how countries evolve across readiness tiers over time, capturing both persistence and mobility in innovation capabilities. The results reveal significant asymmetries in transition probabilities between advanced economies and emerging innovation systems, with several BRICS economies demonstrating gradual upgrading trajectories while others remain structurally locked in lower readiness states. These findings highlight the institutional and policy conditions required to strengthen technology transfer ecosystems. Successful countries in these areas attract foreign investment, participate in global value chains, and profit from technology partnerships. The study contributes to the literature on innovation capability formation and industrial transformation by integrating composite readiness measurement with dynamic transition modelling to inform evidence-based innovation policy. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.06387 |
| By: | Goldberg, Pinelopi Koujianou; Juhász, Réka; Lane, Nathan; Lo Forte, Giulia; Thurk, Jeff |
| Abstract: | The resurgence of subsidies and industrial policies has raised concerns about their potential inefficiency and alignment with multilateral principles. Critics warn that such policies may divert resources to less efficient firms and provoke retaliatory measures from other countries, leading to a wasteful "subsidy race." However, subsidies for sectors with inherent cross-border externalities can have positive global effects. This paper examines these issues within the semiconductor industry: a key driver of economic growth and innovation with potentially significant learning-by-doing and strategic importance due to its dual-use applications. Our study aims to: (1) document and quantify recent industrial policies in the global semiconductor sector, (2) explore the rationale behind these policies, and (3) evaluate their economic impacts, particularly their cross-border effects, and compatibility with multilateral principles. We employ historical analysis, natural language processing, and a model-based approach to measure government support and its impacts. Our findings indicate that government support has been vital for the industry's growth, with subsidies being the primary form of support. They also highlight the importance of cross-border technology transfers through FDI, business and research collaborations, and technology licensing. China, despite significant subsidies, does not stand out as an outlier compared to other countries, given its market size. Preliminary model estimates indicate that while learning-by-doing exists, it is smaller than commonly believed, with significant international spillovers. These spillovers likely reflect cross-country technology transfers and the role of fabless clients in disseminating knowledge globally through their interactions with foundries. Such cross-border spillovers are not merely accidental but result from deliberate actions by market participants that cannot be taken for granted. Firms may choose to share knowledge across borders or restrict access to frontier technology, thereby excluding certain countries. Future research will use model estimates to simulate the quantitative implications of subsidies and to explore the dynamics of a "subsidy race" in the semiconductor industry. |
| Keywords: | Semiconductors; Subsidies; Multilateralism |
| JEL: | F13 F61 L63 N60 O38 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19402 |
| By: | Bastos, Paulo; Stapleton, Katherine; Taglioni, Daria; Wei, Hannah Yi |
| Abstract: | This study examines the role of multinational firms and global value chain linkages in the cross-country diffusion of emerging technologies. The analysis combines detailed information on the near-universe of online job postings in 17 countries with data on multinational networks and firm-to-firm linkages from 2014 to 2022. Online job postings are utilized to investigate how jobs related to emerging technologies spread through firm networks. The findings show that emerging technology jobs are highly concentrated within multinational firms and their supply chains. Approximately one-third of all emerging technology job postings during this period come from Fortune 500 firms, their affiliates, buyers, suppliers, or innovation partners. Although the locations where these technologies originate exhibit a higher prevalence of technology job openings, this advantage diminishes over time as diffusion accelerates in wealthier and geographically closer countries and regions. The study highlights the significant role of firm-to-firm linkages in technology diffusion, with some linkages proving more influential than others. Firms that were previously buyers or innovation partners of establishments in technology-originating locations experienced faster growth in jobs related to these technologies. Moreover, relationships outside corporate boundaries play a particularly critical role, and these connections are influential beyond the factor of geographical distance. |
| JEL: | O33 F23 L14 O14 F14 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19534 |
| By: | Impullitti, Giammario; Licandro, Omar; SedláÄ ek, Petr; Spencer, Adam |
| Abstract: | R&D is procyclical and a crucial driver of growth. Evidence indicates that innovation activity varies widely across firms. Is there heterogeneity in innovation cyclicality? Does innovation heterogeneity matter for business cycle propagation? We provide empirical evidence that more productive firms are less procyclical in innovation. We develop a model replicating this observation, with selection as the driver of heterogeneous innovation cyclicality. We then examine how heterogeneous innovation and growth influence business cycle propagation. Dynamics of firm entry and exit, coupled with heterogeneous cyclicality, significantly amplify TFP shock propagation. Business cycle fluctuations give substantial welfare losses, with firm heterogeneity contributing significantly. |
| Keywords: | Economic growth; Business cycle; Innovation; Heterogeneous firms |
| JEL: | E32 E30 E40 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19385 |
| By: | Bai, Jie; Barwick, Panle; Cao, Shengmao; Li, Shanjun |
| Abstract: | This paper studies the impact of FDI via quid pro quo (technology for market access) in facilitating knowledge spillovers and quality upgrading. Our context is the Chinese automobile industry, where foreign automakers are required to set up joint ventures (JVs) with domestic automakers to facilitate technology transfers in return for market access. Our identification strategy exploits a unique dataset of detailed vehicle quality measures and relies on within-product variation across quality dimensions. We show that affiliated domestic automakers tend to adopt the quality strengths of their JV partners, consistent with knowledge spillovers. We rule out alternative explanations, such as endogenous JV formation, geographic proximity, overlapping customer bases, brand image association, and patent transfers. Additional analysis suggests that worker flows and supplier networks mediate knowledge spillovers. Overall, knowledge spillovers due to ownership affiliation under quid pro quo contributed 8.3% of the quality improvement experienced by affiliated domestic models between 2001 and 2014, relative to nonaffiliated domestic models. |
| JEL: | O14 O25 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19440 |
| By: | Colin Davis (Doshisha University); Ken-ichi Hashimoto (Kobe University); Ken Tabata (Kwansei Gakuin University) |
| Abstract: | This paper studies how environmental policy designed to reduce transboundary pollution affects long-run productivity growth through shifts in the geographic location of industry. We construct a two-country endogenous growth and endogenous market structure framework in which there is a positive link between the geographic concentration of industry and the strength of knowledge spillovers from production to innovation. Emissions are generated as a byproduct of production. We show that an increase in the emissions tax of the country with a larger (smaller) share of industry lowers the concentration of industry leading to weaker (stronger) knowledge spillovers and a slower (faster) rate of productivity growth. In addition, we identify cases where a rise in the emissions tax of the country with a smaller share of industry lowers emissions while increasing productivity growth. With endogenous emissions taxes, a numerical analysis shows that stronger knowledge diffusion leads to higher tax rates, faster productivity growth, and lower global emissions. In contrast, trade liberalization leads to lower tax rates and eventually raises global emissions despite faster productivity growth. Our results highlight that the relationship between productivity growth and global emissions depends critically on the form of economic integration. |
| Keywords: | Asset bubbles; Emissions Taxes, Industry Location, Knowledge Diffusion, Trade Liberalization, Productivity Growth, Global Emissions, Endogenous Market Structure, Endogenous Policy |
| JEL: | F12 O40 Q56 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:kyo:wpaper:1129 |
| By: | Zi Wang (IÉSEG School Of Management [Puteaux]); Ruizhi Yuan (University of Nottingham Ningbo [China]); Boying Li (University of Nottingham Ningbo [China]); V. Kumar (Brock University [Canada]); Ajay Kumar (EM - EMLyon Business School) |
| Abstract: | Financial institutions are increasingly employing artificial intelligence (AI) solutions to optimize their financial advice and services for consumers. However, consumers have demonstrated reluctance toward adopting AI technology goods, and the intermediary psychological mechanism of adoption intention in the financial service context is unclear. Using the theoretical lens of technology affordances and constraints, this article proposes the concept of consumer technology vulnerability (CTV) as the mediating mechanism in the affordance–adoption process of AI financial advisors (AFAs). Meanwhile, consumer innovativeness and self‐efficacy are investigated as individual traits that moderate perceptions and psychological impacts of AI affordances. Specifically, the study first conceptualizes AI affordances in a product innovation context by reviewing the burgeoning literature on AI to date. This is followed by a US‐based survey (N = 616), which shows the positive indirect effects of information optimization, customizability, and human‐likeness on AFA adoption intention through CTV. Self‐efficacy and consumer innovativeness are found to enhance the positive effects of AI affordances on AFA adoption intention through CTV but diminish the impact of human‐likeness on CTV. These findings highlight, for the first time, the mediating role of CTV in new technology adoption. This will help technology innovators and financial institutions to identify how consumers perceive and adopt different AI affordances, and therefore to better incorporate AI characteristics into financial product innovations. |
| Keywords: | AI affordance, AI financial advisor, AI product adoption intention, consumer innovativeness, consumer technology vulnerability |
| Date: | 2026–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05708328 |
| By: | Taghizadeh, Rahim; Babazadeh Behestani, Salar; Arabsheibani, G. Reza |
| Abstract: | Despite extensive research, a consensus remains elusive regarding the optimal method for measuring the effects of technological change and innovation on employment. This study introduces a Technological Change Composite Indicator (TCI), constructed using Principal Component Analysis (PCA) to synthesize seven firm-level innovation metrics. This methodology mitigates issues associated with multicollinearity in regression analyses involving correlated variables. The proposed TCI serves as a proxy for technological change to examine its association with employment in manufacturing sectors across European Union countries. Applying the TCI to a pooled cross‑section of ten European countries and seventeen manufacturing sectors (170 observations) with country fixed effects and a one‑year time lag, we find that a one‑unit increase in the TCI corresponds to a 0.58% higher employment level. The association is positive and statistically significant, indicating that a multidimensional measure of technological change outperforms traditional single proxies such as R&D expenditure or patent counts. The TCI provides policymakers and industry stakeholders with a novel framework for assessing how a broad portfolio of innovation activities—including machinery acquisitions, external knowledge, intellectual property rights, and both product‑ and process‑oriented efforts—relates to manufacturing employment. By moving beyond narrow indicators, our approach offers a more reliable empirical basis for understanding the employment implications of technological change, including emerging technologies such as AI. |
| Keywords: | composite indicator;latent approach;technological change;employment;manufacturing industry;PCA analysis;regression method |
| JEL: | O33 J23 O14 |
| Date: | 2026–07–24 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:140361 |
| By: | Michele Liberatore; Massimo Riccaboni |
| Abstract: | We study how licensing affects the allocation of innovation in pharmaceutical R&D. We develop a model in which projects differ in both quality and innovation regime, distinguishing between incremental and novel innovations. Information precision is higher for incremental projects and lower for novel ones, generating different equilibrium dynamics in the market for technology. The model predicts that licensing sustains positive selection and competitive return equalization for incremental innovation, while novel projects may exhibit weaker screening consistent with lemons-type frictions. Using product-level data and Double Machine Learning methods, we test these predictions across success probabilities and monetary returns. We find that licensing increases success probability overall, but return equalization holds primarily for incremental projects. For novel innovation, licensing does not exhibit the same equilibrium adjustment, suggesting residual market imperfections. Instrumenting for licensing using exogenous pipeline shocks confirms this pattern causally: the competitive risk-return trade-off is preserved for incremental 'rushed' licenses, but it breaks down for novel ones. Our results reconcile evidence on both competitive efficiency and information frictions in markets for technologies, showing that market performance depends systematically on the type of innovation being transacted. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.20365 |
| By: | James Driver |
| Abstract: | This project benefitted the U.S. Census Bureau by the creation of novel, dynamic and data-driven innovation-industry classification systems (i.e., IICSs). The goal of these classification systems is to track a firm’s endogenous choice of innovation and be able to assign it to one, or more, innovation-industries through time. To capture and measure a firm’s innovative output, it is proposed to categorize a firm by its portfolio of patent abstracts from granted, USPTO utility patents where the firm is the original assignee. The idea is to capture the firm responsible for the innovation and to observe the area(s) in which it is innovating. A benefit of utilizing granted patents is that it allows one to compare public and private firms, and their roles in innovation. |
| Keywords: | BERD, LBD, CMF, USPTO |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:cen:tnotes:26-29 |
| By: | C. Castaldi; F. Castellacci; A. Fronzetti Colladon; L. Segneri; F. Venturini |
| Abstract: | Researchers, managers and policymakers are exploring different approaches and data sources to map the development and the diffusion of Artificial Intelligence (AI). In this research note, we illustrate the opportunities offered by trademark data. We argue that AI trademarks can complement AI patents to capture different dimensions of AI innovation. AI trademarks can reveal the extent and ways in which companies exploit AI technologies to develop new goods and services. Importantly, trademark data offer a timely and globally available data source that covers all economic sectors. We present insights from using AI trademarks in an empirical exploration of Italian firms. In our discussion, we reflect on how AI trademarks can be used at different levels of analysis to tackle emerging questions about the development and diffusion of AI. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.18795 |