Firm Business Networks

Abstract

This paper describes an ontology-based approach to integrate datasets from Intellectual Property Australia and the Australian Securities Exchange to study rms in business networks. We combine di erent indicator variables with SPARQL queries for research to understand the characteristics of di erent rms in multiple business networks. We use an exponential random graph models approach to describe factors that help rms form business networks. In doing so we nd evidence of homophily for large rms in patents and trademarks business networks. They are more likely to form business networks in comparison with small & medium rms. For rms in patents and shared director and trademarks and directors business networks, rm size does not play an important factor in the formation of business networks and there is limited evidence of homophily.

Keywords

• semantic web
• business networks
• exponential random graph model

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At a time when governments face budgets constraints, it is important for them and their statistical agencies to make better use of available resources. Governments around the world have realised the advantages of integrating their datasets to use them for purposes beyond which they were collected for. The Australian Government's open data agenda aims to integrate multiple data sources and provide information to encourage evidence-based policy development [ 1 ]. The 2017 Productivity Commission inquiry into Data Availability and Use highlighted the need to create integrated and linked national interest datasets to inform policy development [ 2 ].

The need to integrate a large number of datasets from multiple sources has created a big data challenge for statistical o ces, including the Australian Bureau of Statistics (ABS). The statistical challenges associated with creating and analysing data from diverse sources has been discussed extensively in [ 3 ]. A recent paper by [ 4 ] has presented several ABS case studies on using semantic web technologies to visualise and analyse integrated datasets. This preliminary research builds on [ 5 ]. Using open and purchased data sources, we focus on combining semantic web and statistical methods to develop a better understanding of rms in multiple business networks.

Firms seek partners with complementary assets to leverage each other's strengths and nd competitive advantages to ensure market success. Business networks play a vital role in nding new market opportunities and obtaining the necessary resources to achieve growth [ 6 ]. There are di erent types of business networks ranging from more structured (business groups or franchising) to less structured (R&D consortium, trade association and shared directors). These business networks facilitate di erent degrees of knowledge transfer and create social capital to enhance business performance [ 7 ].

Business networks play a particularly important role in ensuring the economic success of small rms. Firms in business networks have mutual dependence to ensure each other's success. Business networks can also help better resource allocation and reduce operational risks through cooperative arrangements. This is particularly important in sectors with fast technological advancement and short product life cycle. This is evident by the success of high-tech start-ups in Taiwan, where business networks play an important role in integrating the operation of a large number of specialised small rms in subcontracting and outsourcing industries [8, p.2-4].

There is empirical evidence to support rm R&D collaboration as an important source of innovation to improve rm performance [ 9,10 ]. R&D collaboration enables knowledge transfer between rms to share new managerial ideas and technology. Firms look for di erent competitive advantages in the market through business networks [ 11,12 ]. Firms can also form business networks through shared directors. [ 13 ] argued that boards of directors can enhance rm performance through e ectively monitoring and providing resources. [ 14 ] also found that directors serve as an important asset to form business networks, particularly for young high-tech rms.

Governments use a range of initiatives to encourage business networks to achieve economic growth. The Australian Government, through the Department of Industry, Innovation and Science, actively supports rm collaboration. It has established Cooperative Research Centres to encourage collaborative research partnerships between research institutes and industry partners [ 15 ]. Another example is Innovation Connections which provides funding to support collaborative projects between small and medium sized rms to nd expert technology advice and collaborate with research centres in developing new ideas with commercial potential [ 16 ].

It is useful for policy makers to have an understanding of the factors that could lead to the formation of business networks. Are the factors contributing to rms forming business networks di erent when we compare rms in multiple business networks? Are rms with similar characteristics more likely to form business networks? What kind of impact did the global nancial crisis (GFC) have on rms in multiple business networks? This study uses open data and purchased data to address these questions.

Semantic web

We use an ontology-based data model to integrate three datasets - patents, trademarks, and publicly listed companies - to answer our research questions. The patents and trademarks datasets are from 2017 Intellectual Property Government Open Data (IPGOD). The publicly listed companies on shared directors from the Australian Securities Exchange (ASX) is purchased from MorningStar DatAnalysis Premium. Examples of data visualisation can be found in Appendix B.

The semantic web approach is well suited to integrate data from multiple sources and to extract information on rms in multiple business networks. We assign a unique Uniform Resource Identi er (URI) for each rm using a unique rm identi er e.g. Australian Business Numbers (ABNs) or Australian Company Numbers (ACNs). We attach di erent rm attributes e.g. Industry, Pro t and Loss etc. from di erent data sources to each rm. Our datasets include Australian rms in three di erent types of business network relationships: rms that collaborate in R&D related activities (patents networks), rms that collaborate for commercial interests (trademarks networks) and rms that share directors (shared directors networks). We are comparing rms belonging to multiple networks i.e. patents and shared directors networks, patents and trademarks networks and trademarks and shared directors networks. In addition, we separate our sample into three periods: before the global nancial crisis (GFC) from 2003 to 2006, during the GFC from 2007 to 2009 and after the GFC from 2010 to 2013 in our sample.

For our analysis, it is important to know the data provenance to correctly compare rms with and without multiple business networks. We use named graphs in the knowledge graph to distinguish di erent data sources. These named graphs are then used in the SPARQL queries to retrieve the correct subgraph. Figure 1 shows the ontology for our data model. We use Ontodia - an OWL and RDF diagramming tool to visualise our data model. For example, f irm alpha is connected to f irm beta within a patent business network, while it is also connected with f irm gamma in a trademark business network. In comparison, f irm beta is in a patent business network with f irm alpaha and shares a director with f irm delta.

We have 518; 870 triples of rms in the patent named graph http://patents, 5; 638; 915 triples of rms in the trademark named graph http://trademarks and 272; 289 triples in directors named graph http://directors with a total of 6; 430; 074 triples in the integrated knowledge graph. We use legal entities to represent rms. The IPGOD and ASX datasets contain unique rm identi cation numbers (ABNs or ACNs) for rms. We use patent and trademark applications (application number), and directors (director id) to identify rms in di erent business networks. We use unique ABNs and ACNs to form the URI for the legal entities. These URIs serve as unique linking keys to correctly retrieve rm information from di erent sources using SPARQL queries. The bottom right panel of Figure 1 shows the basic business network relationships. The Business Network node quali es how rms can be connected through joint patent or trademark applications or shared directors. An example below shows how we construct a SPARQL query to retrieve rms belonging to both patent and trademark networks in the period before the GFC from 2003 to 2006.

Listing 1.1: intersection SPARQL query prefix pat : <http :// patents > prefix tmk : <http :// trademarks > SELECT ? ABN FROM NAMED pat : FROM NAMED tmk : WHERE { values (? BN) {(" 2003 _BN ") (" 2004 _BN ") (" 2005 _BN ") (" 2006 _BN ")} { GRAPH pat :{ ? LegalEntity fnet : hasAustralianBusinessNumber ? ABN ; fnet : hasBusinessNetwork ? businessNetwork .}} FILTER EXISTS { GRAPH tmk : { ? LegalEntity fnet : hasAustralianBusinessNumber ? ABN ; fnet : hasBusinessNetwork ? businessNetwork .}}}

3 Data sources

3.1 Intellectual Property Government Open Data Patents and trademarks datasets are from the 2017 Intellectual Property Government Open Data (IPGOD) for this study. IPGOD contains administrative information on patents and trademarks [ 17 ]. Patent and trademark applications can be led by one applicant or multiple applicants. Over the sample period between 2003 and 2013, there are 329; 809 applicant-patent application combinations with 290; 568 unique patent applications. In comparison, there are 1; 350; 134 applicant-trademark application combinations with 639; 958 unique trademark applications. Counting unique ABNs and unique ACNs, 15; 617 rms led patent applications and 152; 539 rms led trademark applications over the sample period between 2003 and 2013.

We do not observe whether rms are in business networks or not. However, we observe if a rm les a patent or trademark application by itself or with another rm(s) in IPGOD. Therefore, we de ne a rm as being in a business network in year t when it shares a patent and/or a trademark application with at least one other rm. We create indicator variables equal to one if a rm has a patent and/or trademark application with at lease one other applicant type (small & medium or large enterprises) in year t. The indicator takes zero value if a rm les an application by itself. One would support that the business networks that generate joint patent and/or trademark applications could have existed before year t and could have lasted beyond year t. Consider one scenario, rm A, which had a joint application with rm B in 2003. The last available observation for rm A is in 2005. The network indicator will show rm A was in a business network from 2003 to 2005. We have made this assumption because the duration of a standard patent is 25 years and 10 years for a trademark right [ 18 ][ 19 ]. 3.2

Purchased publicly listed company data

The data on publicly listed companies and their directors is purchased from MorningStar DatAnalysis Premium data service. This service contains detailed reports for all current and formerly listed companies on the Australian Securities Exchange (ASX). There are 1; 722 listed companies and 9; 892 directors in the sample reference period between 2003 and 2013. We use the following data items in our analysis - unique director identi cation number (DirectorID), ABNs, ACNs, Global Industry Classi cation Standard (GICS) industry sectors, GICS industry groups, director appointed dates and director resigned dates.

We create an indicator variable if a rm shares a director with at least one other rm during the sample period. If a director's appointed date is before 01 01 2003, we use 01 01 2003 as the appointed date. We exclude directors who resigned before 01 01 2003. The duration of the shared director network is derived by taking into account the director's earliest appointed and latest resigned dates. For example, if director 001 worked in rm A between 2003 and 2004 and rm B between 2004 and 2005 then rm A and B are connected in the director network between 2003 to 2005. There is a higher proportion of rms with network connections in the ASX data compared to IPGOD. 3.3

Multiple business networks

The scope of the analysis includes rms in three types of multiple business networks. They include when a rm les a joint patent and a joint trademark application with at least one other rm, when a rm les a joint patent application and shares the same director with at least one other rm, and when a rm les a joint trademark application and shares the same director with at least one other rm. Figure 2 compares the proportion of large and small & medium enterprises in these multiple business networks. The number of business networks has grown over time in the sample. Overall, there are no signi cant di erences between the proportion of large and small & medium enterprises over di erent periods. However, we observe a larger increase in the proportion of small & medium enterprises in multiple networks when we compare between the before GFC (2003 to 2006) and during GFC (2007 to 2009) periods.

Statistical model

Our research goal is to describe the factors that contribute to the formation of business networks. There are many interdependent social processes that drive the formation of business networks. The formation of business networks can be in uenced by the presence (or absence) of other ties in the network. The complexity is shown by the business network formed between Verizon Wireless and Google in 2009. Verizon Wireless, one of the key wireless telecommunications providers in the United States, wanted to become less reliant on Apple and iPhone to deliver its service to high-paying customers [ 20 ]. This was mainly because AT&T, one of Verizon Wireless's main competitors, had already established a closed working relationship with Apple [ 21 ]. The successful partnership between Verizon Wireless and Google has led to forming of business networks with other Android phone manufacturers like Samsung [ 22 ].

Business networks are inherently relational so the occurrence of a particular relationship, or tie, could depend on the occurrence of other ties [ 23 ]. The above example clearly demonstrates the endogenous network structural e ects. Firms consider factors beyond simple evaluation of the suitable characteristics of the perspective partners. The decision by Verizon Wireless involves a strategic response to compete against AT&T by forming other ties with Google and Samsung. An observed business network can result from a combination of simultaneous processes with interdependent endogenous factors [ 24 ].

We use an exponential random graph models (ERGMs) approach which takes into account the underlying network structure, characteristics of rms and the characteristics of the dyad in the inference. ERGMs have two main functions: (1) to describe if a given network structure, e.g. edge or transitivity observed in a network, occur more than expected by chance; (2) to determine whether there is an association between network links and rm characteristics and between network links and dyad characteristics or both [ 25 ]. Table 1 shows selected commonly observed business network structures [ 26 ].

Note that the dependency structure is shown when a tie being included in homophily can also be contained in the transitivity structure .

We build on the work of [ 27 ] and [ 28 ] and use ERGMs to study Australian business networks. We use formula (2) in Appendix C to de ne ERGMs as P r(Y = y j ; X) = where y is the observed business network and it takes value 1 if there is a tie between rm i and rm j and 0 otherwise. The symbol represents unknown parameters of interest and determines the e ects of the network statistics. We use g(y; X) to represent the endogenous network statistics in the model. We follow [ 28 ] and include edges and transitivity structures, common network structures observed in business network data. We use X to denote exogenous explanatory variables for the network statistics. There are three rm-speci c characteristics. The variable P roducts is the number of patents or trademark applicants registered by a rm. Our measures of homophily are LargeF irm and SM E. The LargeF irm indicates a tie is formed between two large enterprises. The SM E indicates a tie between two small & medium enterprises. The reference group is a tie form between a LargeF irm and a SM E. The dyad-speci c characteristic, Industry, takes the value one if a network contains at least two rms in the same industry or zero otherwise. The term k( ) is the normalising constant. A discussion on the key ERGMs assumption and basic concept can be found in Appendix C. We use the R ergm package - The Statnet Project to estimate our models. See [ 29 ] for more details. Some useful references and tutorials can be found in [ 30,31 ]. Table 2 shows the statistical model results. The results are in log-odds as discussed in Appendix C. Firms with higher numbers of patent or trademark applications have a slightly higher probability of forming business network. We have some evidence of homophily, i.e. rms with similar characteristics are more likely to form business networks with each other. Table 1 shows that the coe cients for LargeF irm and SM E are signi cant. The probabilities are generally higher for LargeF irm (0:92) than SM E rms (0:73) in particular for rms in patent and trademark business networks in the period before the GFC. We do not observe homophily for rms in trademark and shared director networks as the coe cients for both LargeF irm and SM E are insigni cant in the three periods. There is a mixed result for the same industry indicator variable on rms. Firms operating in same industries appear to have a higher probability of forming trademark and shared director business networks than patent and shared director networks. See model results in Appendix A. 5

Conclusion and future direction

This preliminary analysis has shown the bene ts of using semantic web to integrate datasets to study rms in business networks. We have found that large rms are more likely to form business networks when they are in patents and trademarks business networks. The homophily results are mixed for rms in patents and shared director and trademarks and shared directors business networks. Our research could be extended in several areas. One possibility is to combine the open and purchased datasets with ABS business data to obtain more rm characteristics, such as better industry classi cations or rm productivity to improve the statistical models.Another possibility is to compare the model results with latent class model approach to better understand business network e ects.

Acknowledgements

We gratefully acknowledge Laurent Lefort and Chris Conran, who both provided technical advice for this paper, and Professor Alan Welsh for his comments on the paper.

Appendix A

Model results

Appendix B

Visualisation

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r P sk p < 0 . 0 5 1 1 ; ; 9 9 ) 1 3 ,892 ,952 .(40 .0 .(10 .00 .(10 .705 .(00 .300 .(10 .818 a 0p< .919 .173 )63 088 )40 88 )54 4 )06 3 )93 2 )317 290 dnA 0 S . 1 X

Fig. 4 in Appendix shows the innovation hotspots across Australia. We combine the available latitude and longitude coordinates information with the product type information from 2017 Intellectual Property Government Open Data . Each node represent the location of an applicant. There is missing latitude and longitude information. However, these rms have postcode information. We use a combination of information from the Post O ce, Google map api and a research dataset to impute latitude and longitude information for these rms. The use of postcode is an improvement than using the capital cities for imputing missing latitude and longitude information.

The colour of the node represents the product type for patent or trademark applications. Patent and trademark applications are classi ed using di erent classi cations. This makes it di cult to compare innovations between patent and trademark applications. [ 32 ] proposed an algorithmic links with probabilities (ALP) approach, which analyses the text descriptions, to concord International Patent Classi cation (IPC) for patents and Nice Agreement (NICE) Classi cations for trademarks. We applied their research and concord trademark applications to IPC. A = Human Necessities, B = Performing Operations, Transporting, C = Chemistry, Metallurgy, D = Textiles and Paper, E = Fixed Constructions, F = Me110 chanical E1n2g0ineering, G13=0 Physics, H140= Electrici1t5y0. 160 lon

ERGMs assumptions and basic concept

The exponential random graph models (ERGMs) maximise the probability of the observed networks over the networks with the same number of vertices that could have been observed to estimate parameters. The approach allows statistical inference without independence assumptions. This is because the approach allows for endogenous dependencies coming from the networks. It also contains a set of network statistics that can include exogenous variables coming from the characteristics of vertices or edges [ 33,34 ].

We follow [ 35 ] and specify the general form for an ERGM as:

P (Y = y) = where Y is the random variable for the state of the network and y is the observed networks, g(y; X). We use X to denote the observed rm characteristics. The symbol represents unknown parameters of interest and determines the e ects of the network statistics. The symbol k( ) is the normalising constant, it represents the quantity in the numerator summed over all possible networks (typically constrained to be all networks with the same number of node set as y). The formula (2) can be re-expressed in terms of the conditional log-odds of a single tie between two actors as

logit (Yij = 1 j yi{j) = | (yij); where Yij is the random variable for the state of the rm pair i,j (with realisation yij). We use yi{j to denote the complement of yij, i.e. all connections in the network except yij. The vector (yij) contains the change statistic for each model term. The change statistic records how g(y; X) term changes when yij is toggled from 0 to 1 [ 36 ]. So logit hP r(Yij = 1 j yi{j)i = log (Yij = 1 j yi{j) (Yij = 0 j yi{j) = | (yij)

This means that the coe cients are interpreted as the log-odds of an individual tie conditional on all other ties [ 37 ]. This is the major departure from the logit or probit model. The inclusion is necessary because P r(Yij) is dependent on the dyad-wise outcome of all other dyads [ 23 ]. (2) (3) (4) (5)

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