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IPR policies and determinants of membership in Standard Setting Organizations: a social network analysis


Whereas technical standards and Standard Setting Organizations (SSOs) are omnipresent and essential to mass production and communications, relatively little is formally known about the propensity of firms’ decisions to belong to certain SSOs. An understanding of such propensities can explain why some firms join SSOs (and others do not) and have implications for the regulation of SSOs. This paper uses a social network analysis technique to categorize/place firms in SSO communities and then empirically analyzes their propensities to belong to SSOs. We concentrate our study on standard setting organizations’ features and their intellectual property rights (IPR) policies such as licensing rules, disclosure requirements, as well as the features of the decision process of standards. Using data on more than 1060 member firms as participants in 28 SSOs, we are able to uniquely graph the membership of firms in SSOs by highlighting some important characteristics through community detection. The results provide some novel insights into why firms might choose certain SSO communities over others.

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Fig. 1
Fig. 2

Data availability

The data used in this study are publicly available from the sources listed.


  1. While there are multiple ways to categorize these institutions, three categories are often utilized, i.e., (1) formally recognized standard bodies; (2) quasi-formal standard bodies; and (3) standardization consortia. Whatever the category, it is usually stakeholders that work together on a voluntary basis to produce standards [17]. Thus, SSOs incorporate all variants of groups that develop standards, including Special Interest Groups (SIGs), standard-development organizations, consortia, and other entities.

  2. “Standard Essential Patents (SEPs) are patents that are unavoidable for the implementation of a standardized technology. They represent core, pioneering innovation that entire industries will build upon”,

  3. Pajek is a network analysis software developed by de Nooy et al. [18]. It is a computer program for the analysis and visualization of large networks having numerous vertices (in Slovenian language pajek means ‘spider’), see [6]. There is a variety of software tools that have been developed for social network analysis (see [41]). The most popular software packages include Pajek, UCINET 6, NetDraw, Gephi, E-Net, KeyPlayer 1, StOCNET and Automap (there may also be some pirated versions – see [26]). We employ Pajek in this study because it has efficient algorithms for analyzing large networks in addition to its powerful visualization function(s). See Apostolato [1] for an overview of software applications of social network analysis.

  4. For a list of standards, see The list includes categorized links and overviews of 1068 organizations, and more are added as they are announced.

  5. See

  6. See

  7. See

  8. For instance, in 2014, Unwired Planet, that acquired a portfolio of more than 2,800 patents from Ericsson in 2013, asserted that six of these patents were infringed in the UK against a group of defendants including Huawei, Samsung and Google. Unwired Planet claimed that five of the six patents were essential to a portion of ETSI’s 4G LTE standard. A UK High Court agreed with Unwired Planet’s construction and concluded that some of the patents were essential to the standard and thereby infringed ([16]; also see [27]).

  9. See

  10. We also reference Bekkers and Updegrove ([9]) to obtain additional information for IPR policies that are necessary in our empirical analysis.

  11. Due to exposition limitations in the figure, we note the names of selected member firms in Fig. 1. Also see Table 1. Complete details of the sample firms are available on request.

  12. Our placement of firms in communities may be seen as an empirical complement to the vertically differentiated groups noted by Spulber [40]. Also see Gmati et al. [23]

  13. The Louvain method uses a hierarchical local greedy technique to maximize modularity and is said to be one of the methods with the highest efficiency, both in speed and quality as in [2], and [30]. The method is a greedy optimization method that attempts to optimize the “modularity” of the network (see [2] for details on modularity). The optimization is performed in two steps. First, the method looks for “small” communities by optimizing modularity locally. Second, it aggregates nodes belonging to the same community and builds a new network whose nodes are the communities. These steps are repeated iteratively until a maximum of modularity is attained and a hierarchy of communities is produced [11].

  14. Due to aesthetic issues, we only note the SSOs in Fig. 2; additional details are given in Table 1.

  15. The SCDB database does not distinguish between “royalty-free” and “royalty-free on top of FRAND”. In principle, as indicated by Baron and Spulber [5], royalty-free licensing offers could be unreasonable or discriminatory, e.g. if they included broad cross-licensing requests. However, in our paper, we consider no difference between the two licensing terms.

  16. See the details of the multinomial logit model in Wooldridge [42], p. 644.

  17. The impact of market power or HHI here provides an interesting contrast across communities.

  18. In our sample, Community 4 had the highest average quorum at 0.54 (Table 3).


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We thank a referee for numerous useful suggestions. An earlier version of this paper was circulated as a Kiel Working Paper (#2153).

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Correspondence to Rajeev K. Goel.

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Jiang, J., Goel, R.K. & Zhang, X. IPR policies and determinants of membership in Standard Setting Organizations: a social network analysis. Netnomics 21, 129–154 (2020).

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  • Standard setting organizations
  • Intellectual property rights policies
  • Network analysis
  • Community detection
  • Patents
  • Licensing

JEL code

  • L14
  • O3