Conference Agenda

Drawing on insights from the political economy literature, I argue that the decline of the inner circles cannot be understood without taking economic power of the companies in relation to the economic power of states into consideration. Research that has sought to explain the decline of the corporate inner circles across nations have explained this decline by investigating structural network characteristics, such as the decline of big linkers, and to some extent changes in the political economy by first showing how the financialization led banks to withdraw from the inner circles and secondly how the globalization of the economy led big international companies to withdraw from. Yet other potential drivers such as changes in the national business structure and historical changes in for example firm size, mergers and acquisitions, or ownership structures remain understudied. By empirically investigating the development of the corporate interlock network among the hundred largest Danish companies from 1973 to 2022, I confirm previous findings, by showing a decline of the inner circle from the 1990s onwards that can be explained by a decline in big linkers in the network. The significant decline in the size of the inner circle is however not reflected in the evolution of the accumulated firm size of the inner circle. I therefore argue that the economic power of the inner circle does not fracture but remain stable over time.

The field of competitive dynamics has greatly improved existing understanding of how firms engage in competitive behavior with one another to improve their competitive position. Additionally, competitive dynamics researchers have long studied how organizations engage in dyadic inter-firm rivalry with one another through targeted competitive actions and responses. However, the outcomes of interfirm rivalry affects not only the firms' direct competitors but also evoke reactions from managers of firms beyond the dyad, leading to evolution in the competitive network. Research has long discussed the need to address how the structure of firms’ interorganizational competitive network beyond dyads affects their competitive behavior, along with how firms’ competitive networks lead them to form, maintain, or dissolve competitive ties. Adopting the manager-oriented perspective of competitor identification, this study proposes that numerous factors may impact firms’ perceptions of direct, indirect, and potential competitors, such as the structure of the interorganizational network, the competitive behavior of the firm, and other firm-level attributes. Additionally, this study proposes the usage of statistical models capable of examining change in firms’ competitive network structure, along with methods for examining potential co-evolution mechanisms between the interorganizational competitive network and firms’ competitive behavior. This study contributes to existing research in competitive dynamics by exploring structural, behavioral, and attribute antecedents of firms’ competitive tie formation, leading to an enhanced understanding of how firms identify competitors in their industry.

This study examines how being part of a tightly interconnected cluster of firms – a network community – that has a permeable boundary with bridging ties to firms outside the community influences individual firm innovation performance. Network communities characterized by high permeability—where members maintain bridging ties to external firms—enable the selective importation of diverse knowledge, thereby fostering valuable recombination and innovation. Furthermore, we theorize that the benefits of community permeability are contingent upon the extent to which a firm maintains within-community cohesion and firm-level knowledge heterogeneity. We tested our hypotheses using longitudinal data on alliances and patents from 2002 to 2023. Our findings reveal that firms embedded in network communities with high permeability exhibit enhanced innovation performance. In addition, the benefits of community permeability are amplified when firms are deeply embedded within cohesive community structures and possess highly heterogeneous internal knowledge portfolios. These results highlight that it is not just interfirm network ties that matter for innovation, but the community of network ties a firm is embedded in and the extent to which the community has bridging ties to firms outside the community relationship. This underscores the dual importance of maintaining robust internal community ties for effective knowledge integration while engaging in external linkages that provide access to diverse insights. This research advances the theoretical understanding of the benefits of open and closed networks, reconciling the debate by considering structures between and within network communities.

In a multinational enterprise (MNE), inventor collaborations are essential for the multilateral transfer of technological knowledge, leveraging dispersed expertise, fostering innovation, and maintaining competitive advantage. Previous studies on MNE intra-firm knowledge transfer and innovation applied regression-based statistical analyses to network data, which nevertheless suffers from the violation of independence assumption because of the interdependent nature of organizational members and their interactions. In this study, we adopt the Markov Exponential Random Graph Model (ERGM) approach to examine the co-inventor network within a multinational enterprise (MNE), using data from ARM plc, a semiconductor design MNE, during the 2010–2012 period. The ERGM approach explicitly addresses the interdependencies inherent in network data and accommodates the testing of node- and dyad-level variables. We test the impacts of technological similarity and geographic proximity in the tie formation process while controlling for network structural statistics. Model estimation is performed using Markov Chain Monte Carlo, and simulation results validate the model fit. The findings indicate that co-inventor collaborations are significantly affected by both technological similarity and geographic closeness. This work provides valuable insights into knowledge transfer processes and collaborative innovation, offering a robust and practical framework for analyzing complex network dynamics in MNE.

In this paper we perform a longitudinal ego network analysis of organisational networks containing multiple types of functional actors operating through various jurisdictions. Our data – various corporate debt products issued in Hong Kong – features a range of functionally-specific actors that combine into product ego-networks with rich attribute data. We are interested in how these networks' structures may differ in terms of selection of partnering actors, across product types and jurisdictions, over time. We present a protocol on how to interrogate such data using both existing and novel approaches for analysing ego-networks.