The Impact of Multilevel Networks on Innovation

Introduction of the Article and Authors

The article of interest is entitled “The impact of multilevel networks on innovation” by Chinese authors, namely, Jiancheng Guan and Jingjing Zhang who work at School of Management in University of Chinese Academy of Science and Yan Yan who works at School of Management in Fudan University. Elsevier received the article on 24 April 2014 and published it in the peer-reviewed, Journal of Research Policy, on 14 December 2014.

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Discussion of Main Claim, Goals, Methods, and Findings

Main Claim

The main claim of the article is that innovative activities in the modern society occur due to the existence of extensive and elaborate networks between individuals, organizations, cities, regions, countries, and continents. These networks are drivers of innovation because they allow the flow information, sharing of knowledge, transfer of products, and social interactions at various levels. Despite the existence of extensive and elaborate networks, the article claims that little information is available on the scope and the impact of city-country interactions on innovation (Guan, Zhang & Yan 2015). The understanding of the multilevel interactions is importance for it indicates collaborations between countries and cities in driving innovation. Transport and social networks have a lot of information for researchers have done extensive studies on them. In this view, the article claims that city-country networks constitute the infrastructure of high-level networks, which have a significant influence on low-level networks of innovation.

Goals

The study has two goals of examining how multilevel networks influence innovation in cities and countries. According to Guan, Zhang, and Yan (2015), the goal of the study is to examine the impacts of interaction between country and city’s networks. In providing an elaborate impact of city-country networks on innovations, the study examined how collaborations between cities in a country contribute to the occurrence of innovations in respective cities. In this perspective, the study elucidated how the position of a city in a country and the nature of collaborations determine innovative activities. The study achieved its goals by undertaking extensive literature review focusing on the relationship between the structure of networks and the occurrence of innovative activities. Moreover, the study used models and hypotheses in elucidating processes and mechanisms that influence innovations at country and city’s levels.

Methods

The study used longitudinal design in examining how multilevel networks stimulate innovations in countries and their cities. The study analyzed eight developed countries, which are China, France, Germany, Italy, Japan, Canada, the United States, and Britain. Guan, Zhang, and Yan (2015) obtained a longitudinal dataset from the USPTO database and sampled 41,007 patents showing trends and patterns of patent activities in the sector of alternative energy. Each patent has names and locations of investors such as country, state, and cities, which are vital information for the construction of inter-country and inter-city multilevel networks. The study used the dataset in the USPTO database with information from 1976-2012. Thus, the secondary data used is appropriate because the increasing effects of greenhouse gases such as global warming have led to the establishment of alternative sources of energy, resulting in the increase in technology patents in the recent past. Moreover, as the issue of global warming requires global collaborations among countries, disparate and unique technologies in different countries reflect innovative activities. Guan, Zhang, and Yan (2015) explain that the USPTO database has over 400 categories and 100,000 sub-categories of technologies, which provide important information regarding the trends and patterns of innovations in various countries and cities. Comparison of the knowledge elements of innovation and co-occurrence relations in countries and cities indicates the degree of interlinks between innovative activities.

Findings

The findings show that there is a complex relationship between a city’s patent output and city’s network characteristics. According to Guan, Zhang, and Yan (2015), patent output has statistically significant positive relationship with city’ centrality (r = 0.096, p<0.01), structural holes (r = 0.254, p<0.01), but statistically significant negative relationship with clustering coefficient of city (r = -0.186, p<0.01). The findings imply that centrality of a city determines the occurrence of innovative activities. Cities that occupy central positions experience many innovations because they have advantages of sharing information, knowledge, and technology when compared to cities that are at the periphery. Thus, centrally located cities are hubs of innovations because they attract and accumulate knowledge, information, and innovative activities. The existence of a positive relationship between structural holes and patent output shows the emergence of innovative activities in cities. Guan, Zhang, and Yan (2015) argue that structural holes represent nascent centers of innovations for they are opportunities that attract and accumulate knowledge and information from innovators. In the aspect of clustering, the findings showed that there is a negative relationship between clustering and patent output. Guan, Zhang, and Yan (2015) explain that high clustering decreases innovative activities because it creates a chaotic environment where disorder abounds, traditional ideas dominate, and brokerage lacks, resulting in disrupted sharing of information and knowledge. Therefore, a city with high levels of centrality and structural holes and low level of clustering coefficient attracts and accumulates the highest amounts of knowledge and information, which stimulate innovative activities.

How the Article Supports Its Claims

The article supports its claims by examining literature and analyzing dataset obtained from the USPTO database. In the literature, the article demonstrated that multilevel networks play a significant role in innovation because they create hubs, which promote sharing of information and knowledge. Moreover, the article used theories in supporting the claim that multilevel networks between and within countries and cities stimulate the occurrence of innovative activities. Basing on two theories, the internal and external innovation theory and the interaction theory, Guan, Zhang, and Yan (2015) elucidate their claim. According to the internal and external innovation theory, inter-country networks stimulate city-country networks, which are drivers of innovations at the city’s level. The interaction theory holds that innovators interact with their environment, resulting in the stimulation of innovations in cities.

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The Main Position or Claim Made in Response to the Article

Inter-country and inter-city networks play a significant role in innovation for they provide infrastructure for the flow of information, sharing of knowledge, and the creation of innovation hubs in various cities. From the theoretical perspective, it is evident that external and internal factors of a country or a city are drivers of innovation for they influence the occurrence of innovative activities. The internal and external innovation theory elucidates that knowledge and information flow in and out of countries and cities via multilevel networks. In this view, inter-city and inter-country collaboration networks stimulate the flow of knowledge and information. Fundamentally, innovative activities flow from internal level to national levels as inter-country collaboration networks attract knowledge and information, and then channel them to cities via inter-city collaboration networks. Furthermore, the interaction theory provides the social perspective of how multilevel networks contribute to innovations in countries and cities. In essence, innovators at international, national, and local levels interact and share information and knowledge, which results in the occurrence of innovative activities in cities. The analysis of data obtained from USPTO database proved that centrality of a city and structural holes are central determinants of innovations in cities as reflected by the patent outputs. A centrally located city with many structural holes attracts and accumulates knowledge and information, and thus, stimulating the growth of innovative activities. Moreover, clustering coefficient has a negative relationship with patent output, which means that clustering in countries and cities hampers the occurrence of innovative activities.

Critique of Aspects of the Article

The article has pertinent aspects of a research article because it has abstract, introduction, theory and hypothesis, methodology, results, discussion, and references. The critical review of abstract shows that it provides a comprehensive summary of the article, and thus, enabling readers to understand the nature of the study and its findings. The abstract cites research gap, research objective, the sample size used, and statistical tests employed in data analysis (Guan, Zhang & Yan 2015). Moreover, the abstract summarizes findings and discusses their implications to network and innovation theories. In the aspect of introduction, the study provided extensive literature regarding the impact of multilevel networks on innovative activities in countries and cities. The introduction sets the appropriate context of the study and usher readers to understand variables of the study. In the aspect of theory and hypotheses, the study elucidated how multilevel networks influence the flow of information and knowledge, resulting in the emergence of innovative activities in countries and cities (Guan, Zhang & Yan 2015). Moreover, this aspect also demonstrated formulation of hypotheses by explaining them subsequently. Therefore, the aspect of theory and hypotheses provides literature indicating theoretical and hypothetical frameworks of the study.

The article also has an elaborate methodology showing research design employed, the sampling of data from the USPTO database, and procedure of retrieval (Guan, Zhang & Yan 2015). Moreover, study operationalizes variables by indicating dependent variables, independent variables, and control variables. In addition, the methodology describes statistical models that the study used in data analysis. In the aspect of results, the article tabulated descriptive statistics and correlation coefficients of different variables and interpreted their meaning. Eventually, the article provides the discussion of the findings and their theoretical implications, practical implications, and limitations. In this view, the study contextualized the findings in the current literature and their future impacts on networks and innovations in countries and cities Guan, Zhang & Yan 2015). Analysis of the references shows that the study employed peer-reviewed articles in which most of them are recent sources published in the 21st century.

Scholarly Worth

Critical analysis of the article demonstrates that it has immense scholarly importance because it elucidates how innovative activities occur in countries and cities due to the existence of multilevel networks. Economists, managers, city planners, policymakers, and entrepreneurs are audiences who would get immense benefits from this article because it provides important information regarding the growth of cities in response to the nature of networks that interlink them with other cities, countries, and global markets. As the study identified a subtle gap in research, it opens a gate for future studies focusing on the role of multilevel networks in stimulating innovative activities in countries, cities, and burgeoning cities. Given that the current study only examined the dataset of patents related to alternative energy sources, future research needs to examine patents in other sectors such as software industry, transport industry, computer industry, management systems, and construction industry amongst others.

Reference

Guan, J, Zhang, J & Yan, Y 2015, ‘The impact of multilevel networks on innovation’, Research Policy, vol. 44, no. 1, pp. 545-559.

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