Research on the dual network evolution of the biotechnology industry——Based on temporal exponential random graph model
Modern high-tech companies are simultaneously embedded in various types of networks,yet existing research seldom ana-lyzes how these networks co-evolve.Measured by the strength of relationships,the frequency of interactions within the board of direc-tors is considerably higher compared to the annual general meeting of shareholders.In terms of relationship duration,directors are bound by tenure limits,whereas chain relationships tend to be long-term.Shareholders,on the other hand,may divest their stocks at any time due to market fluctuations,dissolving their shared ownership.In terms of interaction content,major shareholders do not par-ticipate in daily corporate governance and have limited understanding of the business,whereas the board directly engages in corporate governance and strategic decision-making.Consequently,the network of chain directors constitutes a stronger relational network com-pared to the shareholder relationship network,which is relatively weaker.The paper approaches from a dual-network embedding perspective,utilizing data from 2009 to 2021 on 33,845 shareholders and 35,597 directors from 338 biotechnology companies listed in China.It constructs both shareholder relationship networks and chain di-rector networks,employing social network analysis methods and Temporal Exponential Random Graph Models(TERGM)to study the evolutionary paths of organizational dual-network embedding.TERGM considers the interdependence and dependency relationships of networks at different time points,enabling the modeling of various network characteristics like reciprocity,transitivity,and homophily.This is vital for understanding network evolutionary driving mechanisms and allows researchers to explore how different network features influence the formation and dissolution of ties over time.The study finds,firstly,that spatial homophily plays a significant role in the evolution of both networks.Geographically proximate companies are more likely to form chain relationships and shared shareholder relationships.This corroborates previous findings in indus-trial cluster networks and corporate cooperation networks:geographical proximity facilitates the flow of tacit information,fostering coop-eration and trust,thus providing managers with scenarios and opportunities for collaboration.Secondly,in terms of property rights ho-mophily,the two networks exhibit different evolutionary paths.In the chain director network,the nature of state-owned does not affect relationship formation;the establishment of relationships in the shareholder relationship network demonstrates ownership heterogeneity,indicating a higher probability of connection between state-owned and non-state-owned enterprises.as major shareholders seek to diversify their investments to mitigate risks and gain better returns.Thirdly,when examining the evolutionary paths of both networks simultaneously,a cross-network Matthew effect is observed,where the number of relationships a company has in one network positive-ly influences the formation of relationships in the other network,demonstrating mutual dependence and influence.This study extends the concept of preferential attachment in single networks to dual networks,confirming the impact of the Matthew effect in a dual-net-work environment.By adopting a dual network embedding perspective,this study moves beyond the confines of single-network view,reflecting the practical embedding of high-tech enterprises in different networks.It examines the endogenous formation mechanisms of the share-holder relationship network and the interlocking directorate network,offering a clear depiction of the evolutionary trajectory of dual net-works.
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