What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

We analyze the betweenness centrality (BC) of nodes in large complex networks. In general, the BC is increasing with connectivity as a power law with an exponent $\eta$. We find that for trees or networks with a small loop density $\eta=2$ while a larger density of loops leads to $\eta<2$. For scale-free networks characterized by an exponent $\gamma$ which describes the connectivity distribution decay, the BC is also distributed according to a power law with a non universal exponent $\delta$. We show that this exponent $\delta$ must satisfy the exact bound $\delta\geq (\gamma+1)/2$. If the scale free network is a tree, then we have the equality $\delta=(\gamma+1)/2$.

Betweenness Centrality in Large Complex Networks

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Alfred Weber's Theory of the Location of Industries@@@Coastwise Cruising, from Erith to Lowestoft

Inter-organizational relationships involving SMEs: A bibliographic investigation into the state of the art

Complexity Theory: An Overview with Potential Applications for the Social Sciences

Self-organization of industrial clustering in a transition economy: A proposed framework and case study evidence from China

Purpose – Firms in the same industry often display a striking propensity to agglomerate. These geographic concentrations significantly affect innovative behavior of individual firms and therefore have important strategic implications. The purpose of this paper is to report on how networks within the cluster influence firms' commitment to innovation.Design/methodology/approach – Employing a longitudinal case study approach data were collected for 2004 and 2007 about the Shaxi garment cluster in Zhongshan, China. A novel method of measuring innovative behavior was developed and tested at the level of individual small and medium sized enterprises, where R&D expenditure is not recorded, by measuring managerial perception of developments in three areas: product, process, and market development.Findings – The extent to which related firms, various associations, and government policies affected the individual firm's propensity to innovate was examined. The main findings of this paper show firm‐level commitment t...

Cluster network and innovation under transitional economies

This study proposes a probability routing strategy for improving traffic capability on scale-free networks. Compared with the shortest path routing strategy depending on central nodes largely and the efficient routing strategy avoiding hub routers as much as possible, the probability routing strategy makes use of hub routers more efficiently, transferring approximate average amount of packs of the whole network. Simulation results indicate that the probability routing strategy has the highest network capacity among the three routing strategies. This strategy provides network capacity that can be more than 30 times higher than that of the shortest path routing strategy and over 50% higher than that of the efficient routing strategy. In addition, the average routing path length of our proposed strategy is over 10% shorter than that of the efficient routing strategy and only about 10% longer than that of the shortest path routing strategy.

Probability routing strategy for scale-free networks

In this paper, we develop a stochastic process rules (SPR) based Markov chain method to calculate the degree distributions of evolving networks. This new approach overcomes two shortcomings of Shi, Chen and Liu’s use of the Markov chain method (Shi et al. 2005  [21] ). In addition we show how an SPR-based Markov chain method can be effectively used to calculate degree distributions of random birth-and-death networks, which we believe to be novel. First SPR are introduced to replace traditional evolving rules (TR), making it possible to compute degree distributions in one sample space. Then the SPR-based Markov chain method is introduced and tested by using it to calculate two kinds of evolving network. Finally and most importantly, the SPR-based method is applied to the problem of calculating the degree distributions of random birth-and-death networks.

SPR-based Markov chain method for degree distributions of evolving networks

In this paper, a baseline model termed as random birth-and-death network (RBDN) model is considered, in which at each time step, a new node is added into the network with probability p (
 $$0<p<1$$
 ) and connected to m old nodes uniformly, or an existing node is deleted from the network with probability $$q=1-p$$
 . This model allows for fluctuations in size, reflecting the behaviour of networks in many different disciplines including physics, ecology and economics. The purpose of this study is to develop the RBDN model and explore its basic statistical properties. For different p, we first discuss the network size of RBDN, then combining the stochastic process rules based Markov chain method and the probability generating function method, we provide the exact solutions of the degree distributions. Finally, the tail characteristics of the degree distributions are explored after simulation verification. Our results show that the tail of the degree distribution for RBDN exhibits a Poisson tail in the case of $$0<p\le 1/2$$
 and an exponential tail as p approaches to 1.

Zheng He

Papers

Self-organization of industrial clustering in a transition economy: A proposed framework and case study evidence from China

Cluster network and innovation under transitional economies

Probability routing strategy for scale-free networks

SPR-based Markov chain method for degree distributions of evolving networks

Random Birth-and-Death Networks