Characteristics and Evolution of Innovative Collaboration Networks in Architecture, Engineering, and Construction: Study of National Prize-Winning Projects in China

Han, Yilong; Li, Yongkui; Taylor, John E.; Zhong, Jia

doi:10.1061/(asce)co.1943-7862.0001499

Cited by 51 publications

(18 citation statements)

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“…Some studies suggest that when the betweenness is high, it means that a firm can form "bridges" between other network nodes. Thus, firms can promote innovation through discovering asymmetric information [29,44,62]. However, other studies argue that when a firm uses external knowledge resources to innovate, the firm may have little knowledge of how to use the external knowledge resources; the technical support and guidance of the partners is indispensable [63].…”

Section: Discussionmentioning

confidence: 99%

How Does A Firm’s Previous Social Network Position Affect Innovation? Evidence from Chinese Listed Companies

Wei

Chen

2019

Sustainability

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The impact of social network position on innovation has been widely confirmed in past studies. However, research on the time-lag structure of the impact is still insufficient. Within the time window 2010 to 2017, this study constructs a two-mode social network between Chinese listed companies and other participants. To analyze the lag structure of the effect of social network position on innovation, this study uses a panel negative binomial regression model transformed by the Almon polynomial. The results show that a firm does need an advantageous past social network position for innovation. Previous local and global centrality in a social network has a different influence on innovation. For the local centrality indices, degree centrality has a positive impact in the short-term, but has a negative impact in the long-term; the impact of betweenness centrality is not significant in the short-term and is negative in the long run. For the global centrality indices, closeness centrality has a positive influence that decreases with the increase of the time-lag. At the same time, using the method of necessary condition analysis (NCA), this study calculates the bottleneck for a given innovation level. Finally, based on these research conclusions, the theoretical implications and management practice implications are summarized.

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Section: Discussionmentioning

confidence: 99%

How Does A Firm’s Previous Social Network Position Affect Innovation? Evidence from Chinese Listed Companies

Wei

Chen

2019

Sustainability

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“…Enterprises in the center of knowledge network have more ways to obtain innovation resources [45]. When an enterprise uses external resources for innovation, the ability to absorb and use these resources will affect its innovation performance [46]. And the closer an enterprise is to the center of the knowledge network, the more resources it can absorb and use [47].…”

Section: Discussionmentioning

confidence: 99%

City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China

Wang

Yang

Qin

2020

IJERPH

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Based on the panel data of 106 cities in the Yangtze River Economic Belt of China from 2007 to 2016, this paper explores the impact of city centrality on the green innovation efficiency and proves the mediation effect of migrants by using spatial econometric model. The results show that there are more and more innovation contacts between cities, and the innovation network is becoming more and more dense. The core cities of the downstream innovation network are mainly Yangzhou, Zhenjiang, Wuxi, Changzhou, Suzhou and Hangzhou; the core cities in the midstream are mainly Wuhan, Changsha and Yichun; the core cities in the upstream are Chengdu and Bazhong. There is an inverted U-shaped relationship between city centrality and green innovation efficiency. In addition, the influence curve of city centrality on the green innovation efficiency of surrounding cities is also inverted U-shaped. Cities with high city centrality attract a large number of migrants that come from cities with lower centrality to improve the green innovation efficiency, but the green innovation efficiency of cities with low city centrality will decline due to lack of talents. innovation increasingly implies long-term and far-reaching changes in technology, infrastructure, lifestyles and institutions. With the rapid growth of the world's population, living space has been dramatically reduced, and traditional production, manufacturing and business models largely cannot keep up with the pace of economic development. Natural resources are increasingly scarce, and environmental damage is becoming increasingly serious [3]. To reduce environmental pollution and energy consumption, countries are paying growing attention to green technology innovation to achieve the maximum economic and ecological benefits with the lowest levels of consumption and pollution [4]. Chen et al. define green innovation as "hardware or software innovation related to green products or processes, which include technological innovation related to energy conservation, pollution prevention, waste recycling and green product design [5]." Innovation is the fundamental driving force for promoting economic and social development, and green technology innovation is the key driving force for achieving low-carbon economic development and improving the efficiency of natural resources [6]. China has always placed innovation in an important position in the overall development of the country. Relevant data show that the number of R&D personnel in China increased from 304,000 in 2008 to 462,000 in 2017. Internal R&D expenditures over the same period increased from 68.79 billion yuan to 243.57 billion yuan, with an average annual growth rate of 15.08%. Bai [7], however, notes that increasing R&D is a necessary condition for building an innovative country; the focus should be not only on the amount of R&D investment but also on the efficiency of innovation, it is equally salient for developing countries with low levels of science and technology. Green innovation efficiency is a comprehensive effic...

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“…With the continuous increase in scale and complexity, an individual contractor is limited by the resources and information they possess (Son & Rojas, 2011); hence, the success of infrastructure projects requires innovation and collaboration among different stakeholders through-out the project lifecycle (Han et al, 2018). Inter-organizational collaboration is a "must" for innovation (Bossink, 2007;Calamel et al, 2012) and requires highly integrated technologies and the expertise of multiple stakeholders (Loosemore, 2015) to fulfill project requirements.…”

Section: Introductionmentioning

confidence: 99%

“…The requirement for integration of the technologies, expertise, and resources of project stakeholders indicates that collaborative innovation involves intensive interactions (Han et al, 2018), which can have critical impacts on the outcomes of innovation (Baiden et al, 2006). What differentiates the interactions in construction innovation is that the industry is characterized by substantive fragmentation, which complicates the process and, in turn, calls for more interaction (Herrera et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, it is imperative to study the underlying mechanism of interactions among stakeholders in the course of infrastructure project innovation (Vespignani, 2009). Unlike the existing literature, which mainly focuses on coordination between entities (Kearns et al, 2006(Kearns et al, , 2009Hong et al, 2017), this research studies the spread of cooperative behaviors through the interaction processes in innovation during infrastructure project execution because cooperative behaviors are decisive to innovation outcomes and quality (Han et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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The Cascade Effect of Collaborative Innovation in Infrastructure Project Networks

Zhang

Xue

Zhang

2021

Journal of Civil Engineering and Management

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As infrastructure projects get larger and more complex, innovation, which is highly dependent on collaboration and interactions among stakeholders, is critical to meet the challenges. Nevertheless, the existing literature is lacking in terms of studies into the spread of cooperative behaviors in infrastructure project innovations, on which project success is highly contingent. Hence, based on network science theories, this study aims to shed light on the cascade processes of cooperative behaviors in the evolution of collaborative innovations within infrastructure project networks and the impact of various network structures on the cascade effect. The results indicate that the number of initial innovation adopters is positively correlated with the role that the initial adopters play and the cascade effect of collaborative innovation on infrastructure projects. It is also shown that the cascade effect of collaborative innovations is contingent on project network structures. Furthermore, the results also suggest that network connection is positively correlated with cascade speed, scale, and time to reach a stable state. This study is the first to explore the cascade effect of stakeholders’ interactions vis-a-vis collaborative innovation in infrastructure projects. The findings could assist policy-makers and project managers in taking appropriate measures to encourage innovation in infrastructure projects.

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Characteristics and Evolution of Innovative Collaboration Networks in Architecture, Engineering, and Construction: Study of National Prize-Winning Projects in China

Cited by 51 publications

References 1 publication

How Does A Firm’s Previous Social Network Position Affect Innovation? Evidence from Chinese Listed Companies

How Does A Firm’s Previous Social Network Position Affect Innovation? Evidence from Chinese Listed Companies

City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China

The Cascade Effect of Collaborative Innovation in Infrastructure Project Networks

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