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In order to deepen the understanding of self-organization, the purpose of the paper is to raise and analyze the state of the art in the area of innovation networks, particularly the characteristics of self-organizing, relying on the theory of complex systems to overcome any shortcomings.

The databases selected for the search were Web of Science and Scopus; the keywords searched in the titles of articles were innovation networks, complex systems, self-organization and self-organizing; the timeline of the search covers the period between 2000 and 2014 due to the presence of important studies in the field of networks starting in the early 2000s; only studies published in English were used; the articles selected were examined by first reading the titles, then the abstracts, and finally the texts in full.

The way the main constructs from the analytical perspective of innovation networks intersect with complex systems explains how self-organization is presented and how it can be allowed to occur within a view of expected benefits for the purposes of these networks.

The originality of the research lies in the questioning of the classical form of organizational management in innovation networks, essentially based on the concentration of hierarchical power.

This research paper proposes that the conduct of supplier relationships can be improved through a “self‐organized logistical network”.

An analysis has been conducted on logistical supply chain which ensures to define a self‐organized logistical network. In such a network, each supplier can evaluate its own performance by using a decision‐making method involving multiple criteria. Indeed, such method is therefore recommended to reach a satisfactory solution. For that, a typology of performance was proposed and a multiple criteria method was chosen. Indeed, among several methods available, the Analytic Hierarchy Process (AHP) method has been chosen.

The paper suggests quantifying an evaluation of each potential supplier who responds to a call for proposal from a customer, according to rules and criteria that are impartial and common to all. The process enables the emergence of the “best” supplier. The proposed approach allows a balance to be achieved between load and capacity at the supplier level, and produces a smoothing of the load curve among the various suppliers with the long‐term objective of establishing a fair system among the suppliers on the network. Our approach suggests a customer‐supplier (C‐S) relationship control where all entities C‐S partners, communicate and negotiate to respond as best as possible to the customers requirements. To each supplier, we associate a decision‐making centre through which he can self evaluate his performance in order to be able to take part to negotiations within a self organized logistical network.

The research focuses particularly on the study of the decision‐making centre.

This study focuses on the self-organized cooperative consumption of platform participants on social media platform, and reveals how the brand owner cooperates with two-sided customers to achieve value co-creation.

The authors adopted a case study approach to explore how a Chinese beauty startup developed collaborative networks from 2013 to 2022, and tracked the the changes of network structure and cooperation mechanism.

The study finds that the brand owner cooperates with two-sided customers to integrate resources and establish diverse relational trust, which enhances the evolution of a heterogeneous collaborative network for value co-creation.

The study builds upon traditional dyadic actor-to-actor interactions between providers and customers, develops a novel interaction framework of actor-to-network to explain the value co-creation by collaborative networking, reveals the self-organized mechanism of cooperative consumption on social media.

The purpose of this research is to empirically analyse the structure of the Web 2.0 service network and the mechanism behind its evolution over time.

Based on the list of Web 2.0 services and their mashups that is provided on Programmableweb, a network of Web 2.0 services was constructed. Within this network a node represents a Web 2.0 service with an open API, and a link between two nodes represents the existence of a mashup service that uses the two nodes.

The findings suggest that the evolution of the Web 2.0 service network follows the preferential attachment rule although the exponent of the preferential attachment is lower than for other networks following a preferential attachment rule. Additionally the results indicate that the Web 2.0 service network evolves to a scale‐free network but the exponent of the power law distribution is lower than for other networks.

The research applied social network analysis to the Web 2.0 service network. It showed that its network structure and the evolution mechanism are different from those found in similar areas, e.g. the world wide web (WWW). The findings imply that there are factors which lower the exponent of the preferential attachment equation and the power law distribution of the degree centralities.

This paper did not investigate the factors responsible for the low values of the exponent of the preferential attachment equation and the exponent of the power law distribution. However, it is suggested that it could be correlated with the fact that the interconnection between nodes depends on the property of the nodes.

This paper aims to contribute to current research on complexity management by re‐visiting Beer's paradigm on control and self‐organization and explaining its usefulness to support non‐hierarchical organizations and networks and its complementarities to new development in complexity sciences.

The paper explains the current crisis of hierarchical structures and then summarises new proposals on non‐hierarchical organizations from the perspective of complexity sciences. It then summarises Beer's provenance of control and, in particular, the ideas of requisite variety and meta‐systemic management. It explains how these ideas transform the way of approaching management and presents examples of real‐life businesses transformed by following this approach.

The analysis highlights limitations in current management theory and practice that can be overcome by embracing the paradigm of control suggested by some of the pioneer cybernetitians. It shows that the model has unprecedented powers to describe and analyse the network characteristics of contemporary social organizations from the perspective of effective management and lays down a new and democratic paradigm of control.

This paper concentrates on explaining the main arguments of meta‐systemic management suggested originally by Beer and exploring its implications for managing complex networked organizations; more applied research would be convenient to experiment the suggested model.

This study hopefully has shown that core ideas from the tradition of cybernetics add diagnostic and design power to the other tools of complexity management, giving rise to new insights into how learning, networked structures can be brought to reality.

Circular economy (CE) has gained the attention of the business community with the promise of several trillions of dollars to be gained from finding productive uses for waste materials, and developing new business models focused on extending and reimagining the useful life of products. Industrial symbiosis (IS) involves the shared management of resources among multiple firms, most often within some geographic proximity. IS is particularly focused on the reuse of secondary materials, such as industrial by-products, for which conventional recycling does not exist. IS and CE represent a radical reconceptualization of business models from individual to collective competitive advantage with private and public benefits. IS has been recognized as a novel strategy for businesses and regions interested in implementing the CE. In this chapter, we explore the conditions and circumstances in which IS could play a pivotal role in increasing circularity and sustainability in diverse supply networks.

The purpose of this paper is to conceptualize the use of Twitter hashtag as a strategy to enhance the visibility and symbolic power of social movement-related information. It examined how characteristics of hashtag drove information virality during a networked social movement.

Twitter data from two days during the Occupy Wall Street Movement in 2011 were collected. With network analysis, the authors identified popular hashtag types and examined hashtag co-occurrence patterns during the two contrasting movement days. It also provides a comparative analysis of how major types of viral hashtag may play different roles depending on different movement cycles.

The authors found that the role of hashtag influencing information virality may vary based on the context of the tweets. For example, movement participants applied more strategic hashtag combinations during the unexpected event day to reach different social circles. Consistent patterns were identified in mobilizing influential actors such as public figures. Different use patterns of media outlet hashtag were found across the two days.

Implications on how hashtag type and event dynamics may shape hashtag co-occurrence patterns were discussed.

The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy.

A logit regression was applied and the prediction performance in two out‐of‐sample periods, 2007‐2009 and 2010 was examined. On the other hand, feed‐forwards neural networks with Levenberg‐Marquardt error backpropagation algorithm were applied and then neural networks self‐organizing map (SOM) on the training outputs was estimated.

The paper presents the cluster results from SOM training in order to find the patterns of economic recessions and expansions. It is concluded that logit model forecasts the current financial crisis period at 75 percent accuracy, but logit model is useful as it provides a warning signal three quarters before the current financial crisis started officially. Also, it is estimated that the financial crisis, even if it reached its peak in 2009, the economic recession will be continued in 2010 too. Furthermore, the patterns generated by SOM neural networks show various possible versions with one common characteristic, that financial crisis is not over in 2009 and the economic recession will be continued in the USA even up to 2011‐2012, if government does not apply direct drastic measures.

Both logistic regression (logit) and SOMs procedures are useful. The first one is useful to examine the significance and the magnitude of each variable, while the second one is useful for clustering and identifying patterns in economic recessions and expansions.

The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance.

The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007.

BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007.

This study is the first study to investigate credit rating in Jordan using Neural Network technique.

The symbol‐based epistemology used in artificial intelligence is contrasted with the constructivist, coherence epistemology promoted by cybernetics. The latter leads to bootstrapping knowledge representations, in which different parts of the system mutually support each other. Gordon Pask’s entailment meshes are reviewed as a basic application of this approach, and then extended to entailment nets: directed graphs governed by the “bootstrapping axiom”, determining which concepts are to be distinguished or merged. This allows a constant restructuring of the conceptual network. Semantic networks and frame‐like representations can be expressed in this scheme by introducing a basic ontology of node and link types. Entailment nets are then generalized to associative networks with weighted links. Learning algorithms are presented which can adapt the link strengths, based on the frequency with which links are selected by hypertext users. It is argued that such bootstrapping methods can be applied to make the World Wide Web more intelligent, allowing it to self‐organize and support inferences.