nep-cse New Economics Papers
on Economics of Strategic Management
Issue of 2024‒10‒28
seven papers chosen by
João José de Matos Ferreira, Universidade da Beira Interior
  1. Through firms’ eyes: How SMEs define technological spaces and trajectories in the digital era By Monica Plechero; Erica Santini; Giancarlo Coro'
  2. Diagnosis and policy action for sustainable and inclusive productivity growth By OECD
  3. Can the key elements to sustainable transitions be found at the intersection between S3 and new industrial policy? Reflections from the Basque Country case By Edurne Magro Montero; James R. Wilson; Mari Jose Aranguren
  4. Why do we need to complement the European Union Regional Innovation Scoreboard with a cluster-neural network tool for what-if policy analysis? By Vincenzo Lanzetta; Cristina Ponsiglione
  5. Two halves don't make a whole: instability and idleness emerging from the co-evolution of the production and innovation processes By Corentin Lobet; Patrick Llerena; André Lorentz
  6. The Role of Internet Platforms in Voluntary Participation of Users for the Social Innovation Activities By Kim, Moogeon; Ryu, Min Ho
  7. A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy By Xiang Li; Lan Zhao; Junhao Ren; Yajuan Sun; Chuan Fu Tan; Zhiquan Yeo; Gaoxi Xiao
  1. By: Monica Plechero (Dept. of Management, Venice School of Management, Università Ca' Foscari Venice); Erica Santini (Dept. of Economics and Management, Università di Trento); Giancarlo Coro' (Dept. of Economics, Università Ca' Foscari Venice)
    Abstract: How do small and medium-sized enterprises (SMEs) set up their technological portfolio, orient their future technological choices, and contribute to shaping the evolution of a specific economic structure? Technology adoption in SMEs has been recognized as a learning path but how this process is characterized in the digital era remains rather unclear. The paper aims to address this research gap by analysing how a population of manufacturing SMEs builds and follows its technological trajectories. By taking advantage of data on a large sample of manufacturing firms, we employ network analysis to map the evolution in the adoption of digital technologies. Findings show a common path of adoption and learning within the population of manufacturing SMEs. However, while some firms are on the edge, riding the learning curve and providing key meaning to the contextual setting of operations and strategies commonly taken under the technological evolution, others lag in their learning process, risking digital devices.
    Keywords: SMEs, technological trajectory, manufacturing, digital era
    Date: 2023–09
    URL: https://d.repec.org/n?u=RePEc:vnm:wpdman:208
  2. By: OECD
    Abstract: The global productivity slowdown, characterised by a widespread deceleration in aggregate productivity growth rates, is a prevailing concern for policy makers and academics. In this context, this report summarises evidence on productivity growth and business dynamics, highlighting long-term trends and their drivers, as well as insights specific to the COVID-19 period, with relevant implications for future productivity and innovation. It underscores the role of productivity for employment and wages, and discusses challenges related to the digitalisation of the economy and the green transition. Additionally, it considers how the resurgence of industrial policies necessitates additional analysis to measure and coordinate government action.
    Keywords: Artificial intelligence, Business dynamism, COVID-19, Diffusion, Employment, Industrial policy, Innovation, Labour share, Productivity, Technological change
    JEL: J30 L10 O25 O30 O33 L52
    Date: 2024–10–16
    URL: https://d.repec.org/n?u=RePEc:oec:stiaaa:2024/7-en
  3. By: Edurne Magro Montero (Orkestra - Basque Institute of Competitiveness); James R. Wilson (Orkestra - Basque Institute of Competitiveness); Mari Jose Aranguren (Orkestra - Basque Institute of Competitiveness)
    Abstract: The concept of smart specialisation strategies (S3) has dominated the regional policy panorama in the last decade, which implied a shift from neutral and horizontal regional innovation policies towards priority setting in research and innovation. Despite the focus of S3 on research and innovation, we can find some similarities between these strategies and the literature around new industrial policy. The socioeconomic crisis caused by the COVID-19 pandemic highlights the need to adopt a broader view of innovation and industrial policy in which the intertwined green and digital transitions should play a core role. However, this is not an easy task as it implies changes in policy rationales, new instruments, a more entrepreneurial role for government, and a broader, multi-domain and longer-term consideration of intertwined industrial and innovation strategies, among other issues. The aim of this paper is to reflect on the nexus of industrial policies and S3, and the potential that their combination offers for sustainable transitions in the context of experiences in the Basque Country.
    Date: 2022–11–16
    URL: https://d.repec.org/n?u=RePEc:ivc:wpaper:2022r02
  4. By: Vincenzo Lanzetta; Cristina Ponsiglione
    Abstract: The European Union Regional Innovation Scoreboard (EURIS) is currently and broadly used for the definition of regional innovation policies by European policymakers; it is a regional innovation measuring tool for the analysis of each specific innovation indicator, from which it is possible to analyze the overtime evolution of each regional innovation indicator; according to the importance of the European Union Regional Innovation Scoreboard for innovation policy purposes, we state that European regional policymakers need integrative and synergistic methodological tools, with respect to the EURIS one, for innovation policy purposes. Thus, we highlight the need to integrate the current methodology of the European Regional Innovation Scoreboard with a Factorial K-means (FKM) tool for clustering purposes, and with a neural network (NN) tool for performing what-if policy analyses. We claim that our proposed FKM-NN tool could be used, by regional innovation policymakers, as a very effective synergistic instrument of the European Union Regional Innovation Scoreboard.
    Date: 2024–09
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2409.13316
  5. By: Corentin Lobet; Patrick Llerena; André Lorentz
    Abstract: We propose a disaggregated representation of production using an agent-based fund-flow model that emphasizes inefficiencies, such as factor idleness and production instability, and allows us to explore their emergence through simulations. The model incorporates productivity dynamics (learning and depreciation) and is extended with time-saving process innovations. Specifically, we assume workers possess inherent creativity that flourishes during idle periods. The firm, rather than laying off idle workers, is assumed to harness this potential by involving them in the innovation process. Results show that a firm's organizational and managerial decisions, the temporal structure of the production system, the degree of workers' learning and forgetting, and the pace of innovation are critical factors influencing production efficiency in both the short and long term. The co-evolution of production and innovation processes emerges in our model through the two-sided effects of idleness: the loss of skills through forgetting and the deflection of time from the production of goods to the production of ideas giving birth to idleness-driven innovations. In doing so, it allows us to question the status of labour as an adjustment variable in a productive organisation. The paper concludes by discussing potential solutions to this issue and suggesting avenues for future research.
    Keywords: Production Theory; Firm Theory; Agent-based model; Idleness; Innovation; Fund-flow
    Date: 2024–10–09
    URL: https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/27
  6. By: Kim, Moogeon; Ryu, Min Ho
    Keywords: Internet Platform, Social Innovation Activities, SDT, Participatory Platform
    Date: 2024
    URL: https://d.repec.org/n?u=RePEc:zbw:itsb24:302524
  7. By: Xiang Li; Lan Zhao; Junhao Ren; Yajuan Sun; Chuan Fu Tan; Zhiquan Yeo; Gaoxi Xiao
    Abstract: Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involves the creation of a centralized knowledge repository cataloguing historical waste-to-resource transactions, which subsequently enables the generation of recommendations based on past successes. However, a significant barrier to constructing such a knowledge repository lies in the absence of a universally standardized framework for representing business activities across disparate geographical regions. To address this challenge, this paper leverages Large Language Models (LLMs) to classify textual data describing economic activities into the International Standard Industrial Classification (ISIC), a globally recognized economic activity classification framework. This approach enables any economic activity descriptions provided by businesses worldwide to be categorized into the unified ISIC standard, facilitating the creation of a centralized knowledge repository. Our approach achieves a 95% accuracy rate on a 182-label test dataset with fine-tuned GPT-2 model. This research contributes to the global endeavour of fostering sustainable circular economy practices by providing a standardized foundation for knowledge codification and recommendation systems deployable across regions.
    Date: 2024–09
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2409.18988
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