60,153 research outputs found
Rent Appropriation in Strategic Alliances: A Study of Technical Alliances in Pharmaceutical Industry
Many existing alliance studies have investigated how embedded relations create superior value for organizations. The role of network structure in rent appropriation or pie splitting, however, has been underexplored. We propose that favorable locations in interorganizational networks provide firms with superior opportunities for appropriating more economic benefits from alliances than their partners do. Specifically, we argue that partners’ asymmetric network positions will lead to unequal brokerage positions that promote disparate levels of information gathering, monitoring, and bargaining power, which lead to differing capacities to appropriate value. This in turn results in variations in market performance. We also propose this brokerage position exacerbates existing inequalities such as commercial capital; thus, available firm resources will moderate such network effects. Evidence is presented in the form of market response to technology alliance announcements from a set of pharmaceutical firms. In general, we find that firms within central network positions and those spanning structural holes have higher returns than their partners. In addition, we show that this relationship is contingent upon available firm resources
Correlation between centrality metrics and their application to the opinion model
In recent decades, a number of centrality metrics describing network
properties of nodes have been proposed to rank the importance of nodes. In
order to understand the correlations between centrality metrics and to
approximate a high-complexity centrality metric by a strongly correlated
low-complexity metric, we first study the correlation between centrality
metrics in terms of their Pearson correlation coefficient and their similarity
in ranking of nodes. In addition to considering the widely used centrality
metrics, we introduce a new centrality measure, the degree mass. The m order
degree mass of a node is the sum of the weighted degree of the node and its
neighbors no further than m hops away. We find that the B_{n}, the closeness,
and the components of x_{1} are strongly correlated with the degree, the
1st-order degree mass and the 2nd-order degree mass, respectively, in both
network models and real-world networks. We then theoretically prove that the
Pearson correlation coefficient between x_{1} and the 2nd-order degree mass is
larger than that between x_{1} and a lower order degree mass. Finally, we
investigate the effect of the inflexible antagonists selected based on
different centrality metrics in helping one opinion to compete with another in
the inflexible antagonists opinion model. Interestingly, we find that selecting
the inflexible antagonists based on the leverage, the B_{n}, or the degree is
more effective in opinion-competition than using other centrality metrics in
all types of networks. This observation is supported by our previous
observations, i.e., that there is a strong linear correlation between the
degree and the B_{n}, as well as a high centrality similarity between the
leverage and the degree.Comment: 20 page
Learning in Dynamic Inter-firm Networks - The Efficacy of Multiple Contacts
This paper examines the relevance of both an efficiency-based network strategy and a learning-based network strategy in the context of inter-firm partnering. The effect of these different forms of network behaviour on company performance is analysed for companies in the international computer industry. Strategies associated with learning through so-called exploratory networks appear to generate a greater impact on technological performance in a dynamic environment than efficiency strategies through exploitative networks.industrial organization ;
How can innovation economics benefit from complex network analysis?
There is a deficit in economics of theories and empirical data on complex networks, though mathematicians, physicists, biologists, computer scientists, and sociologists are actively engaged in their study. This paper offers a focused review of prominent concepts in contemporary thinking in network research that may motivate further theoretical research and stimulate interest of economists. Possible avenues for modelling innovation, considered the driving force behind economic change, have been explored. A transition is needed from the analysis in economics of the transaction to the explicit examination of market structure and how it processes, or is processed by, innovation.Network; statistics; economy; innovation; modelling
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