Knowledge Spillovers in ICT Industry of India: Evidence from the Firm’s Patent Citation Behavior

Patent citation can be viewed as an indicator for technical impact and technical invention. Highly cited patents represent the “prior art” of many issued patents and are likely to contain significant technological advances. Enterprises that produced these highly cited patents may influence industrial technological development. Because the technologically intensive industries require technology innovation to constantly adapt to the changing environment, any enterprises can disrupt the market and produce high impact technologies. This study aims to explore highly cited technologies in the ICT industry and uses social network analysis and knowledge-based characteristics to investigate the transitions of highly-impact-technology enterprises. The longitudinal analysis of technological leaders examines competitive tendency in specific fields to anchor the positions of the enterprises. This study proposes a different viewpoint to analyze highly-impact-technology enterprises based on social network perspective and knowledge-based characteristics.

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Knowledge Spillovers and Inequality

The American Economic Review ◽

10.1257/000282802762024511 ◽

2002 ◽

Vol 92 (5) ◽

pp. 1290-1307 ◽

Cited By ~ 76

Author(s):

Jan Eeckhout ◽

Boyan Jovanovic

Keyword(s):

Dynamic Model ◽

Knowledge Spillovers ◽

Free Riding ◽

Patent Citation ◽

Citation Data ◽

Free Ride ◽

Catch Up

We develop a dynamic model with knowledge spillovers in production. The model contains two opposing forces. Imitation of other firms helps followers catch up with leaders, but the prospect of doing so makes followers want to free ride. The second force dominates and creates permanent inequality. We show that the greater are the average spillovers and the easier they are to obtain, the greater is the free-riding and inequality. More directed copying raises inequality by raising the free-riding advantages of hanging back. Using Compustat and patent-citation data we find that copying is highly undirected.

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Knowledge Spillovers and Local Industry Growth: A Patent Citation Approach

The American Economist ◽

10.1177/0569434518775102 ◽

2018 ◽

Vol 64 (1) ◽

pp. 60-72

Author(s):

Kejun Song ◽

Gerald Simons ◽

Wei Sun

Keyword(s):

Strong Evidence ◽

Employment Growth ◽

Knowledge Spillovers ◽

Emerging Technology ◽

Patent Citation ◽

Patent Citations ◽

Industry Growth ◽

Metropolitan Statistical Areas ◽

Regional Specialization ◽

Jel Classifications

We create unique patent-based measures of Marshall–Arrow–Romer (MAR) and Jacobs knowledge spillovers using patent citations data and use them to test the Glaeser et al. model of local industry employment growth on three emerging technology categories, namely, computing and communications, drugs and medical, and electrics and electronics. We test growth in 45 U.S. metropolitan statistical areas (MSAs) and consolidated metropolitan statistical areas (CMSAs) for eight two-digit industries over the period 1994 to 2000. We find strong evidence for MAR spillovers from specialization, but little for Jacobs spillovers from diversity. Our results suggest that regional specialization, but not diversity, boosts local industry employment growth in these knowledge fields. JEL classifications: J21, L16

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TECHNOLOGICAL SPILLOVERS THROUGH A PATENT CITATION ANALYSIS

International Journal of Innovation Management ◽

10.1142/s1363919616500286 ◽

2016 ◽

Vol 20 (02) ◽

pp. 1650028 ◽

Cited By ~ 3

Author(s):

LUIGI ALDIERI ◽

CONCETTO PAOLO VINCI

Keyword(s):

Knowledge Spillovers ◽

The United States ◽

Patent Citation ◽

Knowledge Flows ◽

European Patent ◽

Patent Office ◽

Patent Citations ◽

International Firms ◽

Empirical Results ◽

Patent Citation Analysis

The aim of this paper is to analyse the pattern of knowledge flows as evidenced by the patent citations in three economic areas: USA, Japan and Europe. In each economic area, we exploit information from two international patent offices data, the United States Patent and Trademarks Office (USPTO) data and the European Patent Office (EPO) data. In this way, we can investigate the link between the technological proximity and knowledge spillovers for 240 international firms. In particular, the contribution to the existing literature is twofold: First, we use an international sample so that we can compare the empirical results among different economic markets; second, we explore the robustness of results with respect to patent system features. In order to compute the technological proximity, we consider both the symmetrical measure and asymmetrical one. The empirical results indicate that there is a statistically significant correlation between technological proximity and knowledge spillovers measured by patent citations and that these results are robust with respect to patent office data used in the analysis.

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