Cluster performance: an attempt to evaluate the Lithuanian case

Monni, Salvatore; Palumbo, Francesco; Tvaronavičienė, Manuela

doi:10.9770/jesi.2017.5.1(4)

Cited by 28 publications

(23 citation statements)

References 24 publications

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“…Indeed, as Fomina et al [30] argue, the stability of economic development is determined by the features of the network structure in a collaborative engagement of enterprises. Therefore, it is legitimate to explore whether and how the location of companies and other institutions influence their cooperation for the development of innovation, and the performance outcomes thereof.Economic geography has developed the concept of proximity to explain the formation of networks and clusters of innovative companies in industrial districts [31,32]. The different dimensions of proximity may explain some of the positive externalities, such as knowledge creation and transfer, as well as innovation, which are generated by companies that are co-located [33].…”

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confidence: 99%

“…Economic geography has developed the concept of proximity to explain the formation of networks and clusters of innovative companies in industrial districts [31,32]. The different dimensions of proximity may explain some of the positive externalities, such as knowledge creation and transfer, as well as innovation, which are generated by companies that are co-located [33].…”

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confidence: 99%

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Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?

2018

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The involvement of firms in innovation cooperation with different partners has become a widespread phenomenon in the contemporary business landscape. Our paper provides a review of extant alliance, innovation, open innovation and inter-firm collaboration literature and organizes it based on a conceptual framework featuring three levels of analysis: (a) the dyadic level, (b) the network level, and (c) the location level. The article identifies roadmaps in each of these areas and also highlights existing gaps in the present understanding of innovation cooperation. Thereby, it outlines a research agenda by identifying key research questions and issues in the areas where further research is needed and encouraged.Sustainability 2018, 10, 4517 3 of 32 changes that create the economic space of a given country [29] In this space, which Freeman called the National Innovation System, there are more or less formalized networks of cooperating companies and institutions. Indeed, as Fomina et al. [30] argue, the stability of economic development is determined by the features of the network structure in a collaborative engagement of enterprises. Therefore, it is legitimate to explore whether and how the location of companies and other institutions influence their cooperation for the development of innovation, and the performance outcomes thereof.Economic geography has developed the concept of proximity to explain the formation of networks and clusters of innovative companies in industrial districts [31,32]. The different dimensions of proximity may explain some of the positive externalities, such as knowledge creation and transfer, as well as innovation, which are generated by companies that are co-located [33]. According to Boschma [34], proximity implies similarity between actors and organizations, including both a geographical or spatial dimension and other non-spatial dimensions. Knoben and Oerlemans [35] note that "the concept of proximity has been used in many different ways in the literature, this including different measures and definitions".Boschma [34] identifies five types of proximity:

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confidence: 99%

Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?

2018

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“…The important role of the market indicates that technological innovation needs to focus on market orientation, and new energy vehicle enterprises need to connect all kinds of The new energy vehicle industry has all the characteristics of an emerging industry, such that research on the relationship between innovation networks and technological innovation in emerging industries will be beneficial to the new energy vehicle industry. Furthermore, the development mechanism of emerging industry networks has been confirmed by many innovation practices and related theoretical studies to be conducive to aggregating innovation resources, promoting the transformation of scientific and technological achievements, and improving innovation efficiency [22][23][24][25]. Although research on the impact of innovation networks on technological innovation in emerging industries has received much attention, it has been found through the relevant literature that existing studies have the following deficiencies.…”

Section: Introductionmentioning

confidence: 99%

Network Position Advantage and Technological Innovation of China’s New Energy Vehicle Based on the Perspective of Network Theory

et al. 2019

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This paper attempts to explore the role of innovation networks in the new energy vehicle industry from the perspective of evolution, by integrating of the overall network and the entities' microscopic features and designing relative variables. Based on market orientation theory social network analysis, the relationship between network location and technological niche and the role of the network relationship strength was examined through empirical data of China's 2009-2017 patents for new energy vehicles. The results show that: (1) There is an inverted U-shaped relationship between the central position and the technological niche "state" and "potential"; (2) There is an inverted U-shaped relationship between the brokerage position and the entities' technological niche "state", and the inverted U-shaped relationship with the technological niche "potential" is not significant;(3) The overall relationship strength of the network modulates the inverted U relationship between the central location and the technological niche. This paper opens up new ideas for the research of the role of innovation networks. The research conclusions have important implications for the management practice of new energy vehicle industries in China through collaborative networks to achieve technological innovation. entities in the market to broaden their own advantages [3]. It can be said that the close relationship between market orientation and multi-entity collaboration is the main driving factor for technological innovation [4]. Actually, entities have developed innovation networks comprising formal and informal links [5], and taken advantage of the network effect to lead and drive the sustainable development, transformation, and upgrading of the industry. China has formed a variety of innovation networks, such as the National New Energy Vehicle Technological Innovation Collaborating Center, the Intelligent New Energy Vehicle Collaborative Innovation Center, the Beijing-Tianjin-Hebei New Energy Vehicle, and the Intelligent Network Alliance Automotive Collaborative Innovation Alliance. Compared with the innovation networks of traditional industries, the new energy vehicle innovation networks realize the integration of economic network, social network and innovation network. These networks consist of diverse entities whose technical and economic relationships are closer and more complex [6], and the technology transfer within networks is more frequent and the models are flexible [7]. Meanwhile, they can form the basis of resource and knowledge interactions among multiple innovation entities with mutually beneficial effects for technological integration and optimization, thus accelerating the speed and efficiency of innovation for dealing with uncertainty and complexity [8].However, there are many problems in the development of many innovation networks, especially network failure caused by the lack of effective management. Therefore, regardless of the stage in which innovation networks are, it is important to manage them effectively, and t...

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“…It is important to note that CLIs offer information about the expected development of the economy, and thus a debate on the implementation of decisions in the public or private sector can be conducted (Monni et al 2017;Fabuš, 2017;Ciburiene, 2016). This is a short-term estimate of the future economic situation, but it can point to important economic changes which are, in particular, turning points of the economic cycle (Kramoliš, 2015;Mazur, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Composite Leading Indicator for German Business Cycle

Tkáčová¹,

Gavurová²,

Behún³

2017

JOC

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Monitoring and predicting economic cycles have returned to the awareness of economists with the impact of the economic crisis in 2007/2008. To determine the current and future state of the country's economic cycle, Composite Leading Indicators (CLI) can be used. Their structure is being dealt with by institutions at the national and international level (OECD, Eurostat). Correct predictions of public finance development and the entrepreneurship sphere are very important for competitiveness of the country. The aim of the paper is to propose a new Composite Leading Indicator (CLI) to monitor and predict the German economy. The analysis of 140 quantitative and qualitative indicators of industry, services, retail, construction, foreign trade, labor market, money aggregates, stock indices, confidence indicators, consumer expectations was performed for the needs of the indicator. As the reference series represents the German economic cycle, the GDP indicator is selected at constant prices for 2010. All selected quarterly time series are applied with seasonal index methods, the Hodrick-Prescott filter (HP filter) in the R program, cross-correlation with time shifts, methods selection and scoring, data standardization, identification of the same and different data and the subsequent construction of the CLI of the German economic cycle. The generated CLI can predict the German economy cycle two quarters ahead with a cross-correlation value of 0.867. The forecasting capabilities of the assembled indicator were better than the prediction capabilities of OECD, Eurostat and IFO indicator.

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Cluster performance: an attempt to evaluate the Lithuanian case

Cited by 28 publications

References 24 publications

Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?

Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?

Network Position Advantage and Technological Innovation of China’s New Energy Vehicle Based on the Perspective of Network Theory

The Composite Leading Indicator for German Business Cycle

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