Iot innovation clusters in Europe and the case for public policy

By: L. A. Remotti. Cambridge University Press, DOI: 10.1017/dap.2021.16, October 2021.

Abstract: “The Internet of Things (IoT) is currently developing fast and its potential as driver of innovative solutions is increasing, pushed by technologies, networks, communication, and computing power, and has the potential to drive the development of technological ecosystems, such as innovation clusters. Innovation clusters are agglomeration of enterprises and research organizations, which cooperate, interact and compete, generating innovation and driving the growth of ecosystems. The narrative around innovation clusters has been developing since many years and policy-makers seek to use such clusters as a policy instrument to support the growth of technology on the one hand and regional and sectoral development on the other hand. This policy paper expands an empirical study on IoT innovation clusters in Europe and places it within the current debate around clusters and innovation clusters to provide evidence-based advice to policy-makers on what may and may not work as public policy measures. The paper highlights the findings of the interaction with several hundred European IoT innovation clusters and points out their points of view on their own creation factors, operational characteristics, and success stories, as well as their expectations in respect to policy interventions for IoT and for clusters. Suggestions for IoT policy-making are provided. The paper has also undertaken an extensive review of up-to date research on innovation cluster creation and performance, thoroughly analyzing the real possibility to define causal relationships between clusters, productivity and economic growth, and business performance, and providing suggestions for policy-makers on the approach to cluster policy.” [ABSTRACT FROM AUTHOR]

Motorsport Valley revisited: Cluster evolution, strategic cluster coupling and resilience

By: N. Henry, T. Angus, M. Jenkins. European Urban and Regional Studies, DOI: 10.1177/09697764211016039, 2021.

Abstract: “Over 20 years ago a series of papers identified a strikingly dominant economic cluster – the UK’s Motorsport Valley (MSV) – which led to MSV becoming an international exemplar of concepts such as agglomeration, clusters and knowledge-driven systems of regional development. Utilising an evolutionary perspective on cluster development, this paper asks ‘whatever happened to MSV?’. Drawing on the framework of strategic cluster coupling, four cluster development episodes are conceptualised that each depict the dynamic evolution of the cluster’s multi-scalar institutional environment, strategic coupling trajectories and economic development outcomes. Reflecting the emerging synthesis between evolutionary economic geography and geographical political economy, the paper describes an extended case study of cluster development, an evolutionary process of strategic cluster coupling and, ultimately, an example of cluster resilience. Through a focus on strategic cluster coupling, the paper provides further understanding of cluster evolution and path development mechanisms at key moments of cluster reconfiguration – and an empirical update and continuation of the economic story and cluster lifecycle of MSV.” [ABSTRACT FROM AUTHORS]

Evaluating the innovation capability of cluster-based firms: a graph-theoretic approach

By: C. F. Gohr, M. S. de Almeida Tavares, S. N. Morioka. Journal of Business & Industrial Marketing, DOI: 10.1108/JBIM-11-2020-0521">https://doi.org/10.1108/JBIM-11-2020-0521">10.1108/JBIM-11-2020-0521, October 2021.

Abstract: “Purpose – This paper aims to propose an assessment framework to evaluate companies’ innovation capability in the context of industrial clusters. Design/methodology/approach – The assessment framework was built based on the Graph-Theoretic Approach (GTA) to measure the influence of the factors and sub-factors of innovation capabilities. To quantify the level of interdependence between factors and sub-factors of innovation capability Delphi method was adopted. The authors developed five case studies in firms from an Information and Communications Technology and Creative Economy cluster in Northeastern Brazil to test the framework’s applicability. Findings – The results showed that identifying and evaluating the factors of innovation capability allows a larger understanding of what affects these capabilities to a greater or lesser extent and contributes to strategic decision-making. Research limitations/implications – The framework evaluates the innovation capability of each firm, not providing an index for the whole industrial cluster. Besides, the framework does not consider the innovations developed by the companies through the innovation’s capabilities. As the Delphi technique was adopted to analyze the levels of influence or interdependence between factors and sub-factors of innovation capability, different experts may lead to different results. Practical implications – Among the managerial implications, the authors can highlight the innovation capability index as a practical performance measure to stimulate improvement initiatives regarding innovations in industrial clusters. Besides, as the proposed framework is generic, research organizations, public institutions and regional governments can adopt it to analyze innovation capabilities in cluster-based companies. Originality/value – Previous industrial cluster studies have concentrated on knowledge transfer as the main attribute influencing innovation capabilities. The literature also presents assessment frameworks focusing on qualitative analyses or innovation capabilities outcomes (patents and products). Differently, the authors proposed a quantitative assessment framework considering specific factors (and sub-factors) of innovation capabilities in industrial clusters.” [ABSTRACT FROM AUTHORS]

CSR in Clusters: Cluster Social Responsibility

By: A. Zalesna, A. Predygier. The Polish Journal of Economics, DOI: 10.33119/GN/140218, September 2021.

Abstract: “The aim of the article is to identify factors promoting and hindering the implementation of corporate social responsibility (CSR) in a cluster. The analysis is based on the literature of the subject and desk research on clusters, as well as studies by the Polish Agency for Enterprise Development (PARP), the European Commission, selected clusters and the ECCP platform. Factors promoting and hindering the implementation of the CSR concept were analysed at three levels: macro – focusing on the cluster-society relationship; meso – at the level of relations between cluster members; and micro in relation to individual enterprises. The research shows that favourable and unfavourable factors co-exist, which limits the possibilities of implementing the CSR concept in a cluster. Moreover, the concept of CSR at the macro level, in overloaded clusters, requires further elaboration and well-established cooperation of the dominant stakeholders in order to eliminate social problems.” [ABSTRACT FROM AUTHORS]

The dynamizing role of universities in industrial clusters. The case of a Spanish textile cluster

By: F. X. Molina-Morales, L. Martinez-Chafer, J. Capo-Vicedo, J. Capo-Vicedo. The Journal of The Textile Institute, DOI: 10.1080/00405000.2021.1980268, September 2021.

Abstract: “In a new scenario in which globalization has produced a change in the economic contexts of industrial clusters, typically formed by SMEs, the aim of this article is to clarify the role played by universities in the transmission of information and knowledge. In order to do so, we will focus on one of the clusters most affected by the effect of globalization, the Spanish textile cluster, and compare it with another Spanish cluster with higher technological levels. To achieve our goal, we have used Social Network Analysis techniques to analyse the role that two Spanish universities play in their corresponding local industrial clusters. The results offer evidence of the importance of universities, especially when knowledge exchanges are involved, in the clusters that were analysed, regardless of their differences in terms of technological requirements.” [ABSTRACT FROM AUTHORS]

Evaluation of regional industrial cluster innovation capability based on particle swarm clustering algorithm and multi-objective optimization

By: Y. Yan, M. He, L. Song. Complex & Intelligent System, DOI: 10.1007/s40747-021-00521-8, September 2021.

Abstract: “With the progress of the times and the development of science, industrial clusters have been regarded by all countries in the world as one of the important ways to enhance regional competitiveness, and become an inevitable trend of industrial development. The research on the innovation ability of industrial clusters can not only maintain sustainable development of industrial clusters and obtain sustained competitive advantages, but also provide reference for the government's policy formulation of industrial clusters. This paper aims to study the evaluation of regional industrial clusters' innovation capability based on particle swarm clustering and multi-objective optimization. This paper uses the theory of industrial cluster innovation and takes regional industrial system as the empirical research object to establish a regional industrial system capability evaluation system, which is based on the selection of indicators, combined with analytic hierarchy process and factor analysis to evaluate industrial innovation capability. On this basis, the particle swarm clustering theory is used to verify the innovation ability and evaluation index system of industrial clusters, and provide a reference for the evaluation of the innovation ability of industrial clusters. This paper divides the regional cluster innovation capability into four aspects: innovation input capability, environment support capability, self-development capability and innovation output capability, and systematically analyzes the key elements and in the composition of innovation elements and their relationships. It then constructs the evaluation index system of regional cluster innovation capability. At the same time, this paper introduces clustering analysis algorithm and swarm intelligence algorithm into regional innovation evaluation, combines particle swarm optimization algorithm and K-means clustering algorithm, and optimizes particle swarm clustering algorithm by adjusting adaptive parameters and adding fitness variance. The experimental results of this paper show that from the results of the tested innovation potential of the three industrial clusters, industrial cluster F has the strongest innovation ability, with an evaluation coefficient of 0.851, followed by industrial cluster F, which has a value of 0.623. This result is consistent with the actual innovation status of the selected industry. From this point of view, the established particle swarm clustering model for evaluating the innovation capability of regional industrial clusters is reliable and can be used to evaluate the innovation capability of different industrial clusters.” [ABSTRACT FROM AUTHORS]

Competitiveness of Iot industrial clusters based on G2EM-CI model

By: X. Mao, S. Yang. Personal and Ubiquitous Computing, DOI: 10.1007/s00779-021-01645-x, October 2021.

Abstract: “Following the rise of computers, the Internet, and mobile communications, the Internet of Things is the prelude to a new round of industrial development. With the opening of emerging industries related to the Internet of Things, concepts such as smart life have gradually become a reality, changing the traditional way of connecting people to people, increasing the connection between people and things and things and things, and the interconnection of all things is the trend of development. Wisdom life means the process from feeling memory to thinking, which is called “wisdom.” The result of wisdom produces behavior and language. The expression process of behavior and language is called “ability,” and the two are collectively called “intelligence.” In this context, how to improve the competitiveness of the Internet of Things industry has become a hot topic for domestic and foreign scholars. First, it systematically introduces the Internet of Things and its system structure and key technologies and outlines the concepts, characteristics, and industrial chain of the Internet of Things industry on this content. Since the Internet of Things is a new direction that has just started, to promote its sound and rapid development, it is necessary to invest enough energy in systematic cognition and form the basic knowledge of the system of Internet of Things, so as to better guide further development and, secondly, to promote the development of the Internet of Things. Industrial competitiveness is the research object, and the G2EM-CI model theory is used to analyze the factors affecting the competitiveness of China’s Internet of Things industry, so as to better find the theoretical basis that adapts to the actual development; finally, because China’s Internet of Things industry is still exploring in this stage, there are relatively few analyses on the factors affecting the competitiveness of its industry. Studying the factors that affect the competitiveness of my country’s Internet of Things industry, through theoretical analysis and empirical research on them, has very important theoretical significance for the future trend of the Internet of Things industry. Experimental results show that the G2EM-CI model increases the competitiveness of the Internet of Things industry by more than 20%.” [ABSTRACT FROM AUTHORS]

Proposal for integrated management of creative cluster in chosen Slovak region

By: A. Dankova, M. Vrablikova. In THE POPRAD ECONOMIC AND MANAGEMENT FORUM 2021, October 2021.

Abstract: “Creative potential of region is very important for its attractivity – e.g. for living, study, business, employment and tourism. Prešov region (regional level NUTS3) is composed of 13 districts (regional level LAU1), which have different roles in creative regional development (preference of technological innovations and start-ups or focusing on art, culture and history). Aim of this work is to suggest integrated model of management of creative cluster in Prešov region. Integrated management is composed of basic managerial functions (planning, organization, leadership and control) on regional and company level. These levels are connected and in this model is used systematic and project approach. Authors use these methods: analysis, synthesis, induction, deduction, comparative, abstraction and mathematical startitical methods – e. g. indices. Benefits of this work can use potential cluster stakeholders (e. g. creative companies, public administration, universities, art, environmental and tourism organizations, regional development organizations).” [ABSTRACT FROM AUTHORS]