Global networks of genetically modified crops technology: a patent citation network analysis

Abstract

This paper employs the patent data of four major genetically modified (GM) crops, soybeans, cotton, maize and rapeseed, to illustratee how the innovation of GM crop technology diffused and distributed globally over time. Data collected from the Derwent Innovation Index, were employed to construct country patent citation networks, from 1984 to 2015, and the results revealed that developed countries were early adopters, and the primary actors in the innovation of GM crop technology. Only seven developing countries appeared in the country citation network. Most developed countries were reluctant to apply GM crop technology for commercial cultivation. Private businesses stood out in the patent citation network. The early adoption and better performance of developed countries can be explained by the activities of large established private companies.

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Acknowledgements

We would like to thank Brittany N. Anderton for her advice about the keywords searching of genetically modified technology; Billy Liu and Lixiang Wu for their suggestions about network extraction; Joe Egan for English language editing. This work was supported by National Natural Science Foundation of China (NSFC No. 71573241); China Scholarship Council (CSC) Research Program.

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Correspondence to Jianxun Chu.

Appendix: Methodology

Appendix: Methodology

For this research, we studied the innovation diffusion at the organizational and country levels, so organization and country citation networks are constructed (Table 3). Since all the networks are constructed based on the citation relationship, all the networks are fully connected and there are no isolated nodes in the networks.

Table 3 Steps

Step 1: coding of the organizations

We took the mergers and reorganizations of the companies into consideration. The patents belonging to which company were coded based on unique 4-letter code assigned by Derwent (https://images.webofknowledge.com//WOKRS529JR13/help/DII/hs_assignee.html). If the companies share the same 4-letter code, then we coded the companies as one organization (see Table 4).

Table 4 Coding of organizations

If company X is merged or demerged, then there are two categories of codes for company X like X (AAA-C) and X (BBB-C), then owner of the patents will be coded according to the standard codes of company (see Table 5).

Table 5 Coding of organizations

As we are also interested in the how the innovation of GM technology flows between different entities, so all the organizations were coded and assigned to different types based on the information provided on organizations’ official websites. Based on the owners, basic objective and funding, the organizations are classified into 9 types: public sector (including government agency, public research institute and public university), private sector (private business), private university, third sector, public private partnership (PPP), semi-state and government-owned companies, as is shown in Table 6.

Table 6 Types of organizations

Step 2: coding of the countries

In country citation networks, the nodes are the countries, which refer to the patent applicants’ countries of affiliations and the edges the direct citation relationships between the countries (see Table 7).

Table 7 Coding of countries

Step 3: construct the organization citation network

The first step in the construction of the citation network was to clean the patent data. The full record for a patent structured metadata is shown in Table 8. For a single patent record, only patent number(s), patent assignee name(s) and code(s) (organizations filing the patents) and cited patent numbers were extracted from structured meta-data of patent documents (see example in Fig. 14).

Table 8 The full record for a patent structured metadata
Fig. 14
figure14

Example for a patent record. For the set of assignees, only organizations are considered and extracted

Unit of analysis

The networks were constructed for each year. Suppose that time t, containing patents from 1984 to t, is the year the first citation network formed, then the second citation network contains patents from 1984 to t + 1, so the citation networks to be studied include patents: 1984 ~ t, 1984 ~ t + 1, 1984 ~ t + 2, …1984–2015.

Construction of the organization citation networks

\(P = \left\{ {p_{1} ,p_{2} , \ldots p_{n} } \right\}\), n = 5616. \(\forall {\text{i}} \in \left\{ {1, \ldots n} \right\}\). We extracted the patent numbers, organization names and codes, and patents cited by inventors/examiners. So \(P = \left\{ {p_{1} ,p_{2} , \ldots p_{n} } \right\}\), \(\forall {\text{i}} \in \left\{ {1, \ldots n} \right\}\), \(p_{i}\) has 3 data sets as follows:

$$\left\{ {\begin{array}{*{20}c} {A_{i} = \left\{ {a_{1}^{i} ,a_{2}^{i} , \ldots a_{{r_{i} }}^{i} } \right\}} \\ {B_{i} = \left\{ {b_{1}^{i} ,b_{2}^{i} , \ldots b_{{s_{i} }}^{i} } \right\}} \\ {C_{i} = \left\{ {c_{1}^{i} ,c_{2}^{i} , \ldots c_{{t_{i} }}^{i} } \right\}} \\ \end{array} } \right.$$

\(A_{i}\) represents patent numbers, \(B_{i}\) represents organizations (individual-excluded assignees, also referred to assignee), and \(C_{i}\) represents cited patent numbers. If the intersection between data sets \(A_{i}\) and \(C_{j}\) is not an empty set, then patent \(P_{i}\) is cited by patent \(P_{j}\). An arbitrary element (organization) in \(B_{i}\) has a citation relationship with arbitrary element (organization) in \(B_{j}\). The weighted edges are the number of patent citations between the organizations. The first organization citation network formed in 1990, and a total of 26 weighted organization citation networks were constructed (Fig. 3).

Step 4: construct the country citation network

Based on the organization citation networks, country citation networks were constructed. First, we identified the organization’s country, based on the contacted address of the organizations. Then, the MapReduce Paradigm was applied to construct the country citation networks.

Figure 4 illustrates the procedure for constructing the country citation network. First, downloaded patent records are input. As there may be several organizations included in a patent, splitting was conducted, followed by mapping and shuffling. At reducing phase, we have listed all the key-value pairs. The final step is the summary of the reducing results.

Step 5: construct the two-mode network

Two-mode networks focus on two sets of actors, or one set of actors and one set of events. Relations in a two-mode network measure tie between the actors in one set and actors in a second, which means ties existing only between nodes belonging to different sets (Wasserman and Faust 1994, p. 39).

To answer the question “How do different organizations perform in different countries?” two-mode networks were constructed. There are two types of nodes in the network: country and types of organizations. For these networks, there are only connections between countries and social entities. There are no connections among countries or social entities.

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Ji, J., Barnett, G.A. & Chu, J. Global networks of genetically modified crops technology: a patent citation network analysis. Scientometrics 118, 737–762 (2019). https://doi.org/10.1007/s11192-019-03006-1

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Keywords

  • Genetically modified technology
  • Technology diffusion
  • Patent citation network
  • Globalization

Mathematics Subject Classification

  • 91D30

JEL Classification

  • O3