nep-cse New Economics Papers
on Economics of Strategic Management
Issue of 2023‒06‒19
seven papers chosen by
João José de Matos Ferreira
Universidade da Beira Interior

  1. Exploring the drivers of Sustainable Innovation in wine cooperatives: a case-studies analysis By Uliano, Anna; Marotta, Giuseppe; Stanco, Marcello; Nazzaro, Concetta
  2. The Transfer of Federally Funded Technology: A Study of Small, Entrepreneurial, and Ambidextrous Firms By Guerrero, Maribel; Link, Albert; van Hasselt, Martijn
  3. Inward Foreign Direct Investment, Transactions, and Domestic Firms' Performance: Evidence from firm-to-firm transaction linkage By ITO Tadashi; TANAKA Ayumu
  4. Essays on the Adoption and Diffusion of Big Data Analytics and Artificial Intelligence Technology By Nicolas Ameye
  5. Intangible resources and cross-border acquisition decisions: The impact of reputation and the moderating effect of experiential knowledge By Olivier Lamotte; Ludivine Chalençon; Ulrike Mayrhofer; Ana Colovic
  6. Testing an extended knowledge-capital model of foreign direct investment By Kox, Henk L.M.
  7. Productivity Spillovers among Knowledge Workers in Agglomerations: Evidence from GitHub By Lena Abou El-Komboz; Thomas Fackler

  1. By: Uliano, Anna; Marotta, Giuseppe; Stanco, Marcello; Nazzaro, Concetta
    Abstract: With the current social, economical, and environmental scenarios, the intensive farming is no longer viable. In this context, innovation may play a crucial role. In particular, responsible innovation represent a value creation driver, allowing farms to realize internal economies and external social economies. The development of innovative processes is particularly suited to cooperatives, as they generate a competitive advantage and allow to overcome two constraints to sustainable innovation adoption: high costs and complexity. These aspects, which highlight the significant role of cooperation and innovation in the shared value creation process, have not been broadly addressed in previous contributions, especially regarding the wine sector. Therefore, this study aims to investigate the drivers of innovation processes for shared value creation in wine cooperatives. A 2-step analysis was implemented, including the definition of an interpretative model on the drivers of sustainable innovation processes for shared value creation in cooperatives, and a comparative analysis among two wine cooperatives, in order to validate such model. Results have validated the hypothesized scheme: in both cases, the drivers included in the model are essential for the adoption of innovations in viticulture. In particular, governance mechanisms and the very effectiveness of innovations change according to the territorial context.
    Keywords: Environmental Economics and Policy, Research and Development/Tech Change/Emerging Technologies
    Date: 2023–03
  2. By: Guerrero, Maribel (Arizona State University); Link, Albert (University of North Carolina at Greensboro, Department of Economics); van Hasselt, Martijn (University of North Carolina at Greensboro, Department of Economics)
    Abstract: In this paper, we study the technology transfer mechanisms used to protect intellectual property by small, entrepreneurial firms that received Phase II research awards from the U.S Small Business Innovation Research (SBIR) program. The technology transfer mechanisms considered are patenting and publishing. Controlling for the agencies that funded the Phase II research (DOD and NIH), we find that the presence of a university as a research partner engenders greater patenting and publishing. We also find that minority-owned firms patent more intensely than do other firms. A portion of the firms patent and publish; we define these firms, based on our advanced review of the literature, to be ambidextrous. Ambidextrous firms are more likely to include a university as a research partner, to be male-owned and minority-owned, and to be relatively small. Our findings represent a new and important advancement to the literature.
    Keywords: SBIR program; technology transfer; patenting; publishing; intellectual property; ambidexterity; entrepreneurial firms; program evaluation;
    JEL: L21 L26 O34 O38
    Date: 2023–05–30
  3. By: ITO Tadashi; TANAKA Ayumu
    Abstract: Many studies have attempted to use industry-level variations in the presence of foreign firms to estimate the impact of foreign firms on domestic firms. However, owing to the limitations of industry-level data, the channels through which foreign firms influence domestic firms are unclear. Our study used a large set of Japanese firm-to-firm transaction data to test whether domestic firms’ performance improves through firm-to-firm transactions with foreign-affiliated firms. Our empirical analyses using the state-of-the-art technique of causal inference, such as event study design and staggered difference-in-differences estimator, show no evidence of positive spillover effects of MNEs on domestic firms through business transactions.
    Date: 2023–03
  4. By: Nicolas Ameye
    Abstract: Essays on the Adoption and Diffusion of Big Data Analytics and Artificial Intelligence TechnologyThe motivation behind this thesis lies around developing the academic literature, on one hand, on the impact of a specific technology on an organization’s strategy, as well as, on the other hand, on the characteristics and components driving and inhibiting the adoption and diffusion of a specific technology inside an organization.By investigating the drivers and challenges of adopting and diffusing Artificial Intelligence (AI) in an organization, this research aims to answer to the following research question: “What are the main complements and antecedents to the adoption and diffusion of Big Data Analytics and Artificial Intelligence technology, at an organizational level?”.To answer that question, we must first understand how the established models of rank, order, stock and epidemic effects influence the adoption of AI technology. Different streams of works have highlighted four main groups of factors affecting the diffusion of new technologies within or across firms: rank, order, stock and epidemic effects. This thesis examines how these factors influence both the adoption and diffusion of Artificial Intelligence technology across and within firms.Second, this thesis extends the established models to incorporate the effects of uncertainty and competitive intensity on the adoption behaviors of AI technologies among firms. We investigate how uncertainty and competitive intensity affect the adoption behaviours of AI technology among firms.Third, this thesis investigates how technological and managerial complementarities influence the adoption and diffusion of AI technology. To this aim, we extend the established models to incorporate effects of technological and managerial complementarities in the adoption and diffusion of Artificial Intelligence technology among firms. We investigate how technological and managerial complementarities help in facilitating inter-firm diffusion, in driving intra-firm diffusion and in reducing the barriers to AI technology adoption.Fourth, this thesis investigates the discrepancies in adoption and use of AI technology between SMEs and large organizations. To this aim, we explore the determinants and patterns of inter- and intra-firm diffusion at both SMEs and large organization levels.A first finding of this thesis highlights the influence of industry-level adoption on a focal firm’s own adoption. This thesis points at the presence of herding behaviors by which firms tend to follow the crowd. As the share of adopters in the industry increases, the crowd gets bigger and provides a more compelling reason to adopt. However, as our results suggest, these herding behaviors are fragile, exacerbated by competitive forces, and counterbalanced by certain sources of uncertainty while strengthened by others. A second finding of this thesis highlights the importance of pre-existing digital capabilities in the adoption of AI technology. The adoption of AI requires a high degree of maturity and a significant stock of complementary digital technology. This is most likely due to the cumulative nature of AI technology that heavily relies on the information and process infrastructure of the firm. But this implies that leapfrogging on the technology is very unlikely with AI, encouraging firms to build the right foundations (in terms of infrastructure, systems, processes and skills) early on.
    Keywords: Technology adoption; Artificial Intelligence
    Date: 2023–05–25
  5. By: Olivier Lamotte (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie); Ludivine Chalençon (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon); Ulrike Mayrhofer (GRM - Groupe de Recherche en Management - EA 4711 - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - UCA - Université Côte d'Azur); Ana Colovic (NEOMA - Neoma Business School)
    Abstract: Drawing from the resource-based view, signaling theory, and internationalization literatures, we argue that a key intangible resource – reputation – influences the decision to engage in cross-border acquisitions (CBAs). Reputation facilitates foreign market entry by reducing the risks and costs inherent in such strategic moves, while acting to curb potentially risky decisions. Based on a longitudinal sample of 869 acquisitions conducted by European and US firms, our study confirms the inverted U-shaped relationship between a firm's reputation and the likelihood of undertaking CBAs. We also find that international experiential knowledge moderates the relationship between reputation and the likelihood of additional CBAs. Our research contributes to the growing literature on the influence of intangible strategic resources, especially that of reputation, on foreign market entry strategies.
    Keywords: Cross-border acquisitions, information asymmetry, signal, reputation, international experiential knowledge
    Date: 2021–07
  6. By: Kox, Henk L.M.
    Abstract: The knowledge-capital model of foreign direct investment implies that countries with relatively large outward FDI stocks should also have a relative abundance of proprietary knowledge assets. Early versions of the knowledge-capital theory model these assets as if they were only the results of knowledge investments by private firms. We extend the theory by modelling the public-private interaction in knowledge development. This sheds light on the role of the origin country of multinationals. The paper extracts four testable predictions from the model. We to use the inter-country variation in national knowledge-creation systems and foreign-investment performance to test the model. After developing a new dataset that holds knowledge-creation indicators for about 200 countries over the period 2000-2020, we apply a range of non-parametric tests to test the model predictions. Our findings confirm the basic tenet of the knowledge-capital model and show the important role of public knowledge production for outward FDI.
    Keywords: business innovation; public knowledge creation; foreign direct investment; knowledge capital; empirical test; worldwide scope
    JEL: D21 D83 F23 O31 O34
    Date: 2023–05–10
  7. By: Lena Abou El-Komboz (ifo Institute, LMU Munich); Thomas Fackler (ifo Institute, LMU Munich, CESifo, Laboratory for Innovation Science at Harvard)
    Abstract: Software engineering is a field with strong geographic concentration, with Silicon Valley as the epitome of a tech cluster. Yet, most studies on the productivity effects of agglomerations measure innovation with patent data, thus capturing only a fraction of the industry's activity. With data from the open source platform GitHub, our study contributes an alternative proxy for productivity, complementing the literature by covering a broad range of software engineering. With user activity data covering the years 2015 to 2021, we relate cluster size to an individual's productivity. Our findings suggest that physical proximity to a large number of other knowledge workers in the same field leads to spillovers, increasing productivity considerably. In further analyses, we confirm the causal relationship with an IV approach and study heterogeneities by cluster size, initial productivity and project characteristics.
    Keywords: agglomeration effects; knowledge spillovers; open source; online collaboration;
    JEL: D62 J24 O33 O36 R32
    Date: 2023–05–26

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