The city and high-tech startups: The spatial organization of Schumpeterian entrepreneurship

Adler, Patrick; Florida, Richard; King, Karen; Mellander, Charlotta

doi:10.1016/j.cities.2018.12.013

Cited by 101 publications

(61 citation statements)

References 52 publications

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“…This value indicates that there exists a significant spatial autocorrelation. The result is consistent with the findings of a large number of studies [18,25,30]. These studies have found out that innovation tends to be more concentrated than traditional production or manufacturing activities across and within a city and metropolitan area.…”

Section: The Spatial Pattern and Spatial Correlation Of High-tech Firsupporting

confidence: 91%

“…Etzkowitz in his "triple helix model," theorized academia, state, and industry as the three main bodies that form the regional innovation system [63]. The significant influence of universities and research institutions on innovation is confirmed by many studies [25,[64][65][66]. Hamidi examined and verified a significant positive correlation between university R&D and local innovation capabilities [42].…”

Section: The Effects Of Built Environment On the Location Choice And mentioning

confidence: 97%

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Relationship Between the Built Environment and the Location Choice of High-Tech Firms: Evidence from the Pearl River Delta

Wang

et al. 2019

Sustainability

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With the transition in the regional development discourse to knowledge- and innovation-based economics, the cultivation of innovation capacity has gained importance as an initiative to enhance regional sustainability and has emerged as a policy goal. An understanding of the formation of innovation clusters is critical to the cultivation of regional innovation capabilities. Except for the location and regional development conditions’ factors, researchers emphasize a built environment’s role in the formation of innovation clusters. Based on the spatial database of 12,516 high-tech firms in 2017 in the Pearl River Delta (PRD), this study developed a conceptual framework for the built environment. The framework comprises living, service, perceptual, industrial, and physical aspects. The direction and intensity of the correlation between built environment factors and high-tech firms are discussed using the spatial regression model and geographical detector (GD) technique. The results show a highly concentrated spatial distribution pattern of high-tech firms in the PRD. A significant county-level spatial autocorrelation is revealed through Moran’s I. According to the model, we determine the positive impacts of technology support, transport infrastructure, and living service levels on the agglomeration of high-tech firms as well as the negative impact of the public service level. The GD’s result demonstrates different levels of impact intensity of built environmental factors. We argue that a comprehensive understanding of the influence of built environment factors on innovation agglomeration will help policymakers develop targeted policies to foster local innovation capabilities and promote sustainable regional development.

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Section: The Spatial Pattern and Spatial Correlation Of High-tech Firsupporting

confidence: 91%

Section: The Effects Of Built Environment On the Location Choice And mentioning

confidence: 97%

Relationship Between the Built Environment and the Location Choice of High-Tech Firms: Evidence from the Pearl River Delta

Wang

et al. 2019

Sustainability

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“…As a research premise rather than a research question, the concentration of innovative activities can be measured from two contrasting perspectives [23]. From an input perspective, venture capitalists act as crucial Schumpeterian financiers who connect new process innovations with division of labour, enabling the measurement of the geographical variation of commercially relevant innovations [24,25]. Capitals circulate between a discrete set of regions (e.g., from hot money pouring into New York to technology-advanced Silicon Valley) and are network-based to support the localisation of financialisation [26].…”

Section: Spatial Characteristics Of Urban Innovationmentioning

confidence: 99%

“…In general, high-tech innovative firms have a different spatial preference from KIBS firms, i.e., the latter favours city centres, particularly central business districts [43,44]. By contrast, the former are prone to locate in suburbs or even rural areas [25,45]. Nevertheless, discernible trends in several advanced economies have shown that KIBS firms have moved outward to suburban centres, called 'concentrated decentralisation' , and R&D-intensive high-tech firms have started to return downtown in the post-crisis era, giving rise to the 'innovative district' [49,50].…”

Section: Spatial Characteristics Of Urban Innovationmentioning

confidence: 99%

Spatial Evolution and Critical Factors of Urban Innovation: Evidence from Shanghai, China

Zhang

2020

Sustainability

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The critical role of urban innovation in sustaining urban economic resilience has been widely acknowledged by scholars. Yet there is far from a full spectrum of understanding about how innovation performs, despite China’s innovation outputs having far outweighed most countries’. The perennial concern regarding the spatial patterns of innovation has been biased towards the macroscale, and long-standing efforts to explore the determinants of innovative vitality are focused on internal factors (e.g., research and development activities, and firm size). Considering these inadequacies, this research investigates how innovative activities are spatially distributed and how the pattern evolves in cities at the microscale, and examines influencing factors of the external environment. The patent data from 2000 to 2015 in Shanghai are geocoded and mapped into 1 km2 hexagon grids to identify local clustering. Gini coefficient is computed to show the high concentration of innovation activities across space. The hot spot analysis based on the Getis–Ord (Gi*) statistic shows that innovation exhibits a strong concentration propensity at the microscale and gradually moves toward a polycentric pattern. However, the extent of concentration decreases over the study period. Firms dictate innovation activities, and individuals and universities also play a role in downtown innovation growth. The regression using random effect model shows heterogeneous effects on different innovation actors. The overall urban innovation output, dominated by firms, is significantly influenced by public budget expenditures and green space areas. The science and technology grant has a positive impact on authorities but not university and research institutes. This research not only contributes to a methodological innovation for measuring and visualizing an innovation pattern but also enriches our understanding of spatial evolution and critical factors of innovation activities in urban China.

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“…The influence of regional factors on Schumpeterian entrepreneurship is discussed in the article by P. Adler et. al.…”

Section: Features Of Cluster Analysis Of Economic Activitymentioning

confidence: 99%

Cluster Analysis of the Relationship Between Traditional and Innovation Economies

Buleev¹

2019

Montenegrin Journal of Economics

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innovation are developed) are characterised by positive innovation and economic development indicator values. It is also worth noting that this corresponds to the highest value of the "number of people with higher education" indicator. Along with the positive points, this cluster is also distinguished by negatives: the largest number of divorces per 1000 people and the lowest birth rates. The cluster analysis of traditional and innovative economies allows us to formulate the directions of development of economic systems for the formation of harmonious relations between traditional structures and innovative approaches to conducting economic activity.

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The city and high-tech startups: The spatial organization of Schumpeterian entrepreneurship

Cited by 101 publications

References 52 publications

Relationship Between the Built Environment and the Location Choice of High-Tech Firms: Evidence from the Pearl River Delta

Relationship Between the Built Environment and the Location Choice of High-Tech Firms: Evidence from the Pearl River Delta

Spatial Evolution and Critical Factors of Urban Innovation: Evidence from Shanghai, China

Cluster Analysis of the Relationship Between Traditional and Innovation Economies

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